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['Daniel Golovin', 'Andreas Krause', 'Matthew Streeter']

0908.0772v1

Which ads should we display in sponsored search in order to maximize our revenue? How should we dynamically rank information sources to maximize value of information? These applications exhibit strong diminishing returns: Selection of redundant ads and information sources decreases their marginal utility. We show that these and other problems can be formalized as repeatedly selecting an assignment of items to positions to maximize a sequence of monotone submodular functions that arrive one by one. We present an efficient algorithm for this general problem and analyze it in the no-regret model. Our algorithm possesses strong theoretical guarantees, such as a performance ratio that converges to the optimal constant of 1-1/e. We empirically evaluate our algorithm on two real-world online optimization problems on the web: ad allocation with submodular utilities, and dynamically ranking blogs to detect information cascades.

Online Learning of Assignments that Maximize Submodular Functions

http://arxiv.org/pdf/0908.0772v1

Title Online Learning Assignments Maximize Submodular Functions Summary ad display sponsored search order maximize revenue dynamically rank information source maximize value information application exhibit strong diminishing return Selection redundant ad information source decrease marginal utility show problem formalized repeatedly selecting assignment item position maximize sequence monotone submodular function arrive one one present efficient algorithm general problem analyze noregret model algorithm posse strong theoretical guarantee performance ratio converges optimal constant 11e empirically evaluate algorithm two realworld online optimization problem web ad allocation submodular utility dynamically ranking blog detect information cascade Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['C. Balasubramanian', 'K. Duraiswamy']

0908.0984v1

In real life, media information has time attributes either implicitly or explicitly known as temporal data. This paper investigates the usefulness of applying Bayesian classification to an interval encoded temporal database with prioritized items. The proposed method performs temporal mining by encoding the database with weighted items which prioritizes the items according to their importance from the user perspective. Naive Bayesian classification helps in making the resulting temporal rules more effective. The proposed priority based temporal mining (PBTM) method added with classification aids in solving problems in a well informed and systematic manner. The experimental results are obtained from the complaints database of the telecommunications system, which shows the feasibility of this method of classification based temporal mining.

An Application of Bayesian classification to Interval Encoded Temporal mining with prioritized items

http://arxiv.org/pdf/0908.0984v1

Title Application Bayesian classification Interval Encoded Temporal mining prioritized item Summary real life medium information time attribute either implicitly explicitly known temporal data paper investigates usefulness applying Bayesian classification interval encoded temporal database prioritized item proposed method performs temporal mining encoding database weighted item prioritizes item according importance user perspective Naive Bayesian classification help making resulting temporal rule effective proposed priority based temporal mining PBTM method added classification aid solving problem well informed systematic manner experimental result obtained complaint database telecommunication system show feasibility method classification based temporal mining Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Robert Kleinberg', 'Aleksandrs Slivkins']

0911.1174v1

The Lipschitz multi-armed bandit (MAB) problem generalizes the classical multi-armed bandit problem by assuming one is given side information consisting of a priori upper bounds on the difference in expected payoff between certain pairs of strategies. Classical results of (Lai and Robbins 1985) and (Auer et al. 2002) imply a logarithmic regret bound for the Lipschitz MAB problem on finite metric spaces. Recent results on continuum-armed bandit problems and their generalizations imply lower bounds of $\sqrt{t}$, or stronger, for many infinite metric spaces such as the unit interval. Is this dichotomy universal? We prove that the answer is yes: for every metric space, the optimal regret of a Lipschitz MAB algorithm is either bounded above by any $f\in \omega(\log t)$, or bounded below by any $g\in o(\sqrt{t})$. Perhaps surprisingly, this dichotomy does not coincide with the distinction between finite and infinite metric spaces; instead it depends on whether the completion of the metric space is compact and countable. Our proof connects upper and lower bound techniques in online learning with classical topological notions such as perfect sets and the Cantor-Bendixson theorem. Among many other results, we show a similar dichotomy for the "full-feedback" (a.k.a., "best-expert") version.

Sharp Dichotomies for Regret Minimization in Metric Spaces

http://arxiv.org/pdf/0911.1174v1

Title Sharp Dichotomies Regret Minimization Metric Spaces Summary Lipschitz multiarmed bandit MAB problem generalizes classical multiarmed bandit problem assuming one given side information consisting priori upper bound difference expected payoff certain pair strategy Classical result Lai Robbins 1985 Auer et al 2002 imply logarithmic regret bound Lipschitz MAB problem finite metric space Recent result continuumarmed bandit problem generalization imply lower bound sqrtt stronger many infinite metric space unit interval dichotomy universal prove answer yes every metric space optimal regret Lipschitz MAB algorithm either bounded fin omegalog bounded gin osqrtt Perhaps surprisingly dichotomy coincide distinction finite infinite metric space instead depends whether completion metric space compact countable proof connects upper lower bound technique online learning classical topological notion perfect set CantorBendixson theorem Among many result show similar dichotomy fullfeedback aka bestexpert version Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shipra Agrawal', 'Zizhuo Wang', 'Yinyu Ye']

0911.2974v3

A natural optimization model that formulates many online resource allocation and revenue management problems is the online linear program (LP) in which the constraint matrix is revealed column by column along with the corresponding objective coefficient. In such a model, a decision variable has to be set each time a column is revealed without observing the future inputs and the goal is to maximize the overall objective function. In this paper, we provide a near-optimal algorithm for this general class of online problems under the assumption of random order of arrival and some mild conditions on the size of the LP right-hand-side input. Specifically, our learning-based algorithm works by dynamically updating a threshold price vector at geometric time intervals, where the dual prices learned from the revealed columns in the previous period are used to determine the sequential decisions in the current period. Due to the feature of dynamic learning, the competitiveness of our algorithm improves over the past study of the same problem. We also present a worst-case example showing that the performance of our algorithm is near-optimal.

A Dynamic Near-Optimal Algorithm for Online Linear Programming

http://arxiv.org/pdf/0911.2974v3

Title Dynamic NearOptimal Algorithm Online Linear Programming Summary natural optimization model formulates many online resource allocation revenue management problem online linear program LP constraint matrix revealed column column along corresponding objective coefficient model decision variable set time column revealed without observing future input goal maximize overall objective function paper provide nearoptimal algorithm general class online problem assumption random order arrival mild condition size LP righthandside input Specifically learningbased algorithm work dynamically updating threshold price vector geometric time interval dual price learned revealed column previous period used determine sequential decision current period Due feature dynamic learning competitiveness algorithm improves past study problem also present worstcase example showing performance algorithm nearoptimal Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Iza Marfisi-Schottman', 'Aymen Sghaier', 'Sébastien George', 'Franck Tarpin-Bernard', 'Patrick Prévôt']

0911.4262v1

Serious Games (SGs) have experienced a tremendous outburst these last years. Video game companies have been producing fun, user-friendly SGs, but their educational value has yet to be proven. Meanwhile, cognition research scientist have been developing SGs in such a way as to guarantee an educational gain, but the fun and attractive characteristics featured often would not meet the public's expectations. The ideal SG must combine these two aspects while still being economically viable. In this article, we propose a production chain model to efficiently conceive and produce SGs that are certified for their educational gain and fun qualities. Each step of this chain will be described along with the human actors, the tools and the documents that intervene.

Towards Industrialized Conception and Production of Serious Games

http://arxiv.org/pdf/0911.4262v1

Title Towards Industrialized Conception Production Serious Games Summary Serious Games SGs experienced tremendous outburst last year Video game company producing fun userfriendly SGs educational value yet proven Meanwhile cognition research scientist developing SGs way guarantee educational gain fun attractive characteristic featured often would meet public expectation ideal SG must combine two aspect still economically viable article propose production chain model efficiently conceive produce SGs certified educational gain fun quality step chain described along human actor tool document intervene Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hartmut Neven', 'Vasil S. Denchev', 'Geordie Rose', 'William G. Macready']

0912.0779v1

In a previous publication we proposed discrete global optimization as a method to train a strong binary classifier constructed as a thresholded sum over weak classifiers. Our motivation was to cast the training of a classifier into a format amenable to solution by the quantum adiabatic algorithm. Applying adiabatic quantum computing (AQC) promises to yield solutions that are superior to those which can be achieved with classical heuristic solvers. Interestingly we found that by using heuristic solvers to obtain approximate solutions we could already gain an advantage over the standard method AdaBoost. In this communication we generalize the baseline method to large scale classifier training. By large scale we mean that either the cardinality of the dictionary of candidate weak classifiers or the number of weak learners used in the strong classifier exceed the number of variables that can be handled effectively in a single global optimization. For such situations we propose an iterative and piecewise approach in which a subset of weak classifiers is selected in each iteration via global optimization. The strong classifier is then constructed by concatenating the subsets of weak classifiers. We show in numerical studies that the generalized method again successfully competes with AdaBoost. We also provide theoretical arguments as to why the proposed optimization method, which does not only minimize the empirical loss but also adds L0-norm regularization, is superior to versions of boosting that only minimize the empirical loss. By conducting a Quantum Monte Carlo simulation we gather evidence that the quantum adiabatic algorithm is able to handle a generic training problem efficiently.

Training a Large Scale Classifier with the Quantum Adiabatic Algorithm

http://arxiv.org/pdf/0912.0779v1

Title Training Large Scale Classifier Quantum Adiabatic Algorithm Summary previous publication proposed discrete global optimization method train strong binary classifier constructed thresholded sum weak classifier motivation cast training classifier format amenable solution quantum adiabatic algorithm Applying adiabatic quantum computing AQC promise yield solution superior achieved classical heuristic solver Interestingly found using heuristic solver obtain approximate solution could already gain advantage standard method AdaBoost communication generalize baseline method large scale classifier training large scale mean either cardinality dictionary candidate weak classifier number weak learner used strong classifier exceed number variable handled effectively single global optimization situation propose iterative piecewise approach subset weak classifier selected iteration via global optimization strong classifier constructed concatenating subset weak classifier show numerical study generalized method successfully competes AdaBoost also provide theoretical argument proposed optimization method minimize empirical loss also add L0norm regularization superior version boosting minimize empirical loss conducting Quantum Monte Carlo simulation gather evidence quantum adiabatic algorithm able handle generic training problem efficiently Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shailendra Singh', 'Sanjay Silakari']

0912.1014v1

Feature selection is an indispensable preprocessing step when mining huge datasets that can significantly improve the overall system performance. Therefore in this paper we focus on a hybrid approach of feature selection. This method falls into two phases. The filter phase select the features with highest information gain and guides the initialization of search process for wrapper phase whose output the final feature subset. The final feature subsets are passed through the Knearest neighbor classifier for classification of attacks. The effectiveness of this algorithm is demonstrated on DARPA KDDCUP99 cyber attack dataset.

An ensemble approach for feature selection of Cyber Attack Dataset

http://arxiv.org/pdf/0912.1014v1

Title ensemble approach feature selection Cyber Attack Dataset Summary Feature selection indispensable preprocessing step mining huge datasets significantly improve overall system performance Therefore paper focus hybrid approach feature selection method fall two phase filter phase select feature highest information gain guide initialization search process wrapper phase whose output final feature subset final feature subset passed Knearest neighbor classifier classification attack effectiveness algorithm demonstrated DARPA KDDCUP99 cyber attack dataset Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Daniel M. Kane']

0912.2709v1

We provide asymptotically sharp bounds for the Gaussian surface area and the Gaussian noise sensitivity of polynomial threshold functions. In particular we show that if $f$ is a degree-$d$ polynomial threshold function, then its Gaussian sensitivity at noise rate $\epsilon$ is less than some quantity asymptotic to $\frac{d\sqrt{2\epsilon}}{\pi}$ and the Gaussian surface area is at most $\frac{d}{\sqrt{2\pi}}$. Furthermore these bounds are asymptotically tight as $\epsilon\to 0$ and $f$ the threshold function of a product of $d$ distinct homogeneous linear functions.

The Gaussian Surface Area and Noise Sensitivity of Degree-$d$ Polynomials

http://arxiv.org/pdf/0912.2709v1

Title Gaussian Surface Area Noise Sensitivity Degreed Polynomials Summary provide asymptotically sharp bound Gaussian surface area Gaussian noise sensitivity polynomial threshold function particular show f degreed polynomial threshold function Gaussian sensitivity noise rate epsilon le quantity asymptotic fracdsqrt2epsilonpi Gaussian surface area fracdsqrt2pi Furthermore bound asymptotically tight epsilonto 0 f threshold function product distinct homogeneous linear function Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['O. J. Oyelade', 'O. O. Oladipupo', 'I. C. Obagbuwa']

1002.2425v1

The ability to monitor the progress of students academic performance is a critical issue to the academic community of higher learning. A system for analyzing students results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k mean clustering algorithm for analyzing students result data. The model was combined with the deterministic model to analyze the students results of a private Institution in Nigeria which is a good benchmark to monitor the progression of academic performance of students in higher Institution for the purpose of making an effective decision by the academic planners.

Application of k Means Clustering algorithm for prediction of Students Academic Performance

http://arxiv.org/pdf/1002.2425v1

Title Application k Means Clustering algorithm prediction Students Academic Performance Summary ability monitor progress student academic performance critical issue academic community higher learning system analyzing student result based cluster analysis us standard statistical algorithm arrange score data according level performance described paper also implemented k mean clustering algorithm analyzing student result data model combined deterministic model analyze student result private Institution Nigeria good benchmark monitor progression academic performance student higher Institution purpose making effective decision academic planner Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vitaly Feldman']

1002.3183v3

Statistical query (SQ) learning model of Kearns (1993) is a natural restriction of the PAC learning model in which a learning algorithm is allowed to obtain estimates of statistical properties of the examples but cannot see the examples themselves. We describe a new and simple characterization of the query complexity of learning in the SQ learning model. Unlike the previously known bounds on SQ learning our characterization preserves the accuracy and the efficiency of learning. The preservation of accuracy implies that that our characterization gives the first characterization of SQ learning in the agnostic learning framework. The preservation of efficiency is achieved using a new boosting technique and allows us to derive a new approach to the design of evolutionary algorithms in Valiant's (2006) model of evolvability. We use this approach to demonstrate the existence of a large class of monotone evolutionary learning algorithms based on square loss performance estimation. These results differ significantly from the few known evolutionary algorithms and give evidence that evolvability in Valiant's model is a more versatile phenomenon than there had been previous reason to suspect.

A Complete Characterization of Statistical Query Learning with Applications to Evolvability

http://arxiv.org/pdf/1002.3183v3

Title Complete Characterization Statistical Query Learning Applications Evolvability Summary Statistical query SQ learning model Kearns 1993 natural restriction PAC learning model learning algorithm allowed obtain estimate statistical property example cannot see example describe new simple characterization query complexity learning SQ learning model Unlike previously known bound SQ learning characterization preserve accuracy efficiency learning preservation accuracy implies characterization give first characterization SQ learning agnostic learning framework preservation efficiency achieved using new boosting technique allows u derive new approach design evolutionary algorithm Valiants 2006 model evolvability use approach demonstrate existence large class monotone evolutionary learning algorithm based square loss performance estimation result differ significantly known evolutionary algorithm give evidence evolvability Valiants model versatile phenomenon previous reason suspect Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['G. Nathiya', 'S. C. Punitha', 'M. Punithavalli']

1004.1743v1

Clustering is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. It is used for the exploration of inter-relationships among a collection of patterns, by organizing them into homogeneous clusters. Clustering has been dynamically applied to a variety of tasks in the field of Information Retrieval (IR). Clustering has become one of the most active area of research and the development. Clustering attempts to discover the set of consequential groups where those within each group are more closely related to one another than the others assigned to different groups. The resultant clusters can provide a structure for organizing large bodies of text for efficient browsing and searching. There exists a wide variety of clustering algorithms that has been intensively studied in the clustering problem. Among the algorithms that remain the most common and effectual, the iterative optimization clustering algorithms have been demonstrated reasonable performance for clustering, e.g. the Expectation Maximization (EM) algorithm and its variants, and the well known k-means algorithm. This paper presents an analysis on how partition method clustering techniques - EM, K -means and K* Means algorithm work on heartspect dataset with below mentioned features - Purity, Entropy, CPU time, Cluster wise analysis, Mean value analysis and inter cluster distance. Thus the paper finally provides the experimental results of datasets for five clusters to strengthen the results that the quality of the behavior in clusters in EM algorithm is far better than k-means algorithm and k*means algorithm.

An Analytical Study on Behavior of Clusters Using K Means, EM and K* Means Algorithm

http://arxiv.org/pdf/1004.1743v1

Title Analytical Study Behavior Clusters Using K Means EM K Means Algorithm Summary Clustering unsupervised learning method constitutes cornerstone intelligent data analysis process used exploration interrelationship among collection pattern organizing homogeneous cluster Clustering dynamically applied variety task field Information Retrieval IR Clustering become one active area research development Clustering attempt discover set consequential group within group closely related one another others assigned different group resultant cluster provide structure organizing large body text efficient browsing searching exists wide variety clustering algorithm intensively studied clustering problem Among algorithm remain common effectual iterative optimization clustering algorithm demonstrated reasonable performance clustering eg Expectation Maximization EM algorithm variant well known kmeans algorithm paper present analysis partition method clustering technique EM K mean K Means algorithm work heartspect dataset mentioned feature Purity Entropy CPU time Cluster wise analysis Mean value analysis inter cluster distance Thus paper finally provides experimental result datasets five cluster strengthen result quality behavior cluster EM algorithm far better kmeans algorithm kmeans algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ankur Moitra', 'Gregory Valiant']

1004.4223v1

Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has a running time, and data requirement polynomial in the dimension and the inverse of the desired accuracy, with provably minimal assumptions on the Gaussians. As simple consequences of our learning algorithm, we can perform near-optimal clustering of the sample points and density estimation for mixtures of k Gaussians, efficiently. The building blocks of our algorithm are based on the work Kalai et al. [STOC 2010] that gives an efficient algorithm for learning mixtures of two Gaussians by considering a series of projections down to one dimension, and applying the method of moments to each univariate projection. A major technical hurdle in Kalai et al. is showing that one can efficiently learn univariate mixtures of two Gaussians. In contrast, because pathological scenarios can arise when considering univariate projections of mixtures of more than two Gaussians, the bulk of the work in this paper concerns how to leverage an algorithm for learning univariate mixtures (of many Gaussians) to yield an efficient algorithm for learning in high dimensions. Our algorithm employs hierarchical clustering and rescaling, together with delicate methods for backtracking and recovering from failures that can occur in our univariate algorithm. Finally, while the running time and data requirements of our algorithm depend exponentially on the number of Gaussians in the mixture, we prove that such a dependence is necessary.

Settling the Polynomial Learnability of Mixtures of Gaussians

http://arxiv.org/pdf/1004.4223v1

Title Settling Polynomial Learnability Mixtures Gaussians Summary Given data drawn mixture multivariate Gaussians basic problem accurately estimate mixture parameter give algorithm problem running time data requirement polynomial dimension inverse desired accuracy provably minimal assumption Gaussians simple consequence learning algorithm perform nearoptimal clustering sample point density estimation mixture k Gaussians efficiently building block algorithm based work Kalai et al STOC 2010 give efficient algorithm learning mixture two Gaussians considering series projection one dimension applying method moment univariate projection major technical hurdle Kalai et al showing one efficiently learn univariate mixture two Gaussians contrast pathological scenario arise considering univariate projection mixture two Gaussians bulk work paper concern leverage algorithm learning univariate mixture many Gaussians yield efficient algorithm learning high dimension algorithm employ hierarchical clustering rescaling together delicate method backtracking recovering failure occur univariate algorithm Finally running time data requirement algorithm depend exponentially number Gaussians mixture prove dependence necessary Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mikhail Belkin', 'Kaushik Sinha']

1004.4864v1

The question of polynomial learnability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and machine learning. However, despite major progress, the general question of polynomial learnability of Gaussian mixture distributions still remained open. The current work resolves the question of polynomial learnability for Gaussian mixtures in high dimension with an arbitrary fixed number of components. The result on learning Gaussian mixtures relies on an analysis of distributions belonging to what we call "polynomial families" in low dimension. These families are characterized by their moments being polynomial in parameters and include almost all common probability distributions as well as their mixtures and products. Using tools from real algebraic geometry, we show that parameters of any distribution belonging to such a family can be learned in polynomial time and using a polynomial number of sample points. The result on learning polynomial families is quite general and is of independent interest. To estimate parameters of a Gaussian mixture distribution in high dimensions, we provide a deterministic algorithm for dimensionality reduction. This allows us to reduce learning a high-dimensional mixture to a polynomial number of parameter estimations in low dimension. Combining this reduction with the results on polynomial families yields our result on learning arbitrary Gaussian mixtures in high dimensions.

Polynomial Learning of Distribution Families

http://arxiv.org/pdf/1004.4864v1

Title Polynomial Learning Distribution Families Summary question polynomial learnability probability distribution particularly Gaussian mixture distribution recently received significant attention theoretical computer science machine learning However despite major progress general question polynomial learnability Gaussian mixture distribution still remained open current work resolve question polynomial learnability Gaussian mixture high dimension arbitrary fixed number component result learning Gaussian mixture relies analysis distribution belonging call polynomial family low dimension family characterized moment polynomial parameter include almost common probability distribution well mixture product Using tool real algebraic geometry show parameter distribution belonging family learned polynomial time using polynomial number sample point result learning polynomial family quite general independent interest estimate parameter Gaussian mixture distribution high dimension provide deterministic algorithm dimensionality reduction allows u reduce learning highdimensional mixture polynomial number parameter estimation low dimension Combining reduction result polynomial family yield result learning arbitrary Gaussian mixture high dimension Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Adam Gauci', 'Kristian Zarb Adami', 'John Abela']

1005.0390v2

In this work, decision tree learning algorithms and fuzzy inferencing systems are applied for galaxy morphology classification. In particular, the CART, the C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable classifiers are developed to distinguish between spiral galaxies, elliptical galaxies or star/unknown galactic objects. Morphology information for the training and testing datasets is obtained from the Galaxy Zoo project while the corresponding photometric and spectra parameters are downloaded from the SDSS DR7 catalogue.

Machine Learning for Galaxy Morphology Classification

http://arxiv.org/pdf/1005.0390v2

Title Machine Learning Galaxy Morphology Classification Summary work decision tree learning algorithm fuzzy inferencing system applied galaxy morphology classification particular CART C45 Random Forest fuzzy logic algorithm studied reliable classifier developed distinguish spiral galaxy elliptical galaxy starunknown galactic object Morphology information training testing datasets obtained Galaxy Zoo project corresponding photometric spectrum parameter downloaded SDSS DR7 catalogue Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sumit Ganguly']

1005.1120v2

For each $p \in (0,2]$, we present a randomized algorithm that returns an $\epsilon$-approximation of the $p$th frequency moment of a data stream $F_p = \sum_{i = 1}^n \abs{f_i}^p$. The algorithm requires space $O(\epsilon^{-2} \log (mM)(\log n))$ and processes each stream update using time $O((\log n) (\log \epsilon^{-1}))$. It is nearly optimal in terms of space (lower bound $O(\epsilon^{-2} \log (mM))$ as well as time and is the first algorithm with these properties. The technique separates heavy hitters from the remaining items in the stream using an appropriate threshold and estimates the contribution of the heavy hitters and the light elements to $F_p$ separately. A key component is the design of an unbiased estimator for $\abs{f_i}^p$ whose data structure has low update time and low variance.

Estimating small moments of data stream in nearly optimal space-time

http://arxiv.org/pdf/1005.1120v2

Title Estimating small moment data stream nearly optimal spacetime Summary p 02 present randomized algorithm return epsilonapproximation pth frequency moment data stream Fp sumi 1n absfip algorithm requires space Oepsilon2 log mMlog n process stream update using time Olog n log epsilon1 nearly optimal term space lower bound Oepsilon2 log mM well time first algorithm property technique separate heavy hitter remaining item stream using appropriate threshold estimate contribution heavy hitter light element Fp separately key component design unbiased estimator absfip whose data structure low update time low variance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ankan Saha', 'Ambuj Tewari']

1005.2146v1

Cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning. Reasons for this include its simplicity, speed and stability, as well as its competitive performance on $\ell_1$ regularized smooth optimization problems. Surprisingly, very little is known about its finite time convergence behavior on these problems. Most existing results either just prove convergence or provide asymptotic rates. We fill this gap in the literature by proving $O(1/k)$ convergence rates (where $k$ is the iteration counter) for two variants of cyclic coordinate descent under an isotonicity assumption. Our analysis proceeds by comparing the objective values attained by the two variants with each other, as well as with the gradient descent algorithm. We show that the iterates generated by the cyclic coordinate descent methods remain better than those of gradient descent uniformly over time.

On the Finite Time Convergence of Cyclic Coordinate Descent Methods

http://arxiv.org/pdf/1005.2146v1

Title Finite Time Convergence Cyclic Coordinate Descent Methods Summary Cyclic coordinate descent classic optimization method witnessed resurgence interest machine learning Reasons include simplicity speed stability well competitive performance ell1 regularized smooth optimization problem Surprisingly little known finite time convergence behavior problem existing result either prove convergence provide asymptotic rate fill gap literature proving O1k convergence rate k iteration counter two variant cyclic coordinate descent isotonicity assumption analysis proceeds comparing objective value attained two variant well gradient descent algorithm show iterates generated cyclic coordinate descent method remain better gradient descent uniformly time Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Volker Nannen']

1005.2364v2

The concept of overfitting in model selection is explained and demonstrated with an example. After providing some background information on information theory and Kolmogorov complexity, we provide a short explanation of Minimum Description Length and error minimization. We conclude with a discussion of the typical features of overfitting in model selection.

A Short Introduction to Model Selection, Kolmogorov Complexity and Minimum Description Length (MDL)

http://arxiv.org/pdf/1005.2364v2

Title Short Introduction Model Selection Kolmogorov Complexity Minimum Description Length MDL Summary concept overfitting model selection explained demonstrated example providing background information information theory Kolmogorov complexity provide short explanation Minimum Description Length error minimization conclude discussion typical feature overfitting model selection Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andri Mirzal', 'Masashi Furukawa']

1005.2603v5

This paper presents a concise tutorial on spectral clustering for broad spectrum graphs which include unipartite (undirected) graph, bipartite graph, and directed graph. We show how to transform bipartite graph and directed graph into corresponding unipartite graph, therefore allowing a unified treatment to all cases. In bipartite graph, we show that the relaxed solution to the $K$-way co-clustering can be found by computing the left and right eigenvectors of the data matrix. This gives a theoretical basis for $K$-way spectral co-clustering algorithms proposed in the literatures. We also show that solving row and column co-clustering is equivalent to solving row and column clustering separately, thus giving a theoretical support for the claim: ``column clustering implies row clustering and vice versa''. And in the last part, we generalize the Ky Fan theorem---which is the central theorem for explaining spectral clustering---to rectangular complex matrix motivated by the results from bipartite graph analysis.

Eigenvectors for clustering: Unipartite, bipartite, and directed graph cases

http://arxiv.org/pdf/1005.2603v5

Title Eigenvectors clustering Unipartite bipartite directed graph case Summary paper present concise tutorial spectral clustering broad spectrum graph include unipartite undirected graph bipartite graph directed graph show transform bipartite graph directed graph corresponding unipartite graph therefore allowing unified treatment case bipartite graph show relaxed solution Kway coclustering found computing left right eigenvectors data matrix give theoretical basis Kway spectral coclustering algorithm proposed literature also show solving row column coclustering equivalent solving row column clustering separately thus giving theoretical support claim column clustering implies row clustering vice versa last part generalize Ky Fan theoremwhich central theorem explaining spectral clusteringto rectangular complex matrix motivated result bipartite graph analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['James P. Crutchfield', 'Sean Whalen']

1005.2714v2

We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream teacher and then pass samples from the model to their downstream student. It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process using structured populations with memory. We examine the diffusion and fixation properties of several drift processes and propose applications to learning, inference, and evolution. We also demonstrate how the organization of drift process space controls fidelity, facilitates innovations, and leads to information loss in sequential learning with and without memory.

Structural Drift: The Population Dynamics of Sequential Learning

http://arxiv.org/pdf/1005.2714v2

Title Structural Drift Population Dynamics Sequential Learning Summary introduce theory sequential causal inference learner chain estimate structural model upstream teacher pas sample model downstream student extends population dynamic genetic drift recasting Kimuras selectively neutral theory special case generalized drift process using structured population memory examine diffusion fixation property several drift process propose application learning inference evolution also demonstrate organization drift process space control fidelity facilitates innovation lead information loss sequential learning without memory Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Pierre-François Marteau', 'Sylvie Gibet']

1005.5141v12

This paper proposes some extensions to the work on kernels dedicated to string or time series global alignment based on the aggregation of scores obtained by local alignments. The extensions we propose allow to construct, from classical recursive definition of elastic distances, recursive edit distance (or time-warp) kernels that are positive definite if some sufficient conditions are satisfied. The sufficient conditions we end-up with are original and weaker than those proposed in earlier works, although a recursive regularizing term is required to get the proof of the positive definiteness as a direct consequence of the Haussler's convolution theorem. The classification experiment we conducted on three classical time warp distances (two of which being metrics), using Support Vector Machine classifier, leads to conclude that, when the pairwise distance matrix obtained from the training data is \textit{far} from definiteness, the positive definite recursive elastic kernels outperform in general the distance substituting kernels for the classical elastic distances we have tested.

On Recursive Edit Distance Kernels with Application to Time Series Classification

http://arxiv.org/pdf/1005.5141v12

Title Recursive Edit Distance Kernels Application Time Series Classification Summary paper proposes extension work kernel dedicated string time series global alignment based aggregation score obtained local alignment extension propose allow construct classical recursive definition elastic distance recursive edit distance timewarp kernel positive definite sufficient condition satisfied sufficient condition endup original weaker proposed earlier work although recursive regularizing term required get proof positive definiteness direct consequence Hausslers convolution theorem classification experiment conducted three classical time warp distance two metric using Support Vector Machine classifier lead conclude pairwise distance matrix obtained training data textitfar definiteness positive definite recursive elastic kernel outperform general distance substituting kernel classical elastic distance tested Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['P. Bouboulis']

1005.5170v1

The present report, has been inspired by the need of the author and its colleagues to understand the underlying theory of Wirtinger's Calculus and to further extend it to include the kernel case. The aim of the present manuscript is twofold: a) it endeavors to provide a more rigorous presentation of the related material, focusing on aspects that the author finds more insightful and b) it extends the notions of Wirtinger's calculus on general Hilbert spaces (such as Reproducing Hilbert Kernel Spaces).

Wirtinger's Calculus in general Hilbert Spaces

http://arxiv.org/pdf/1005.5170v1

Title Wirtingers Calculus general Hilbert Spaces Summary present report inspired need author colleague understand underlying theory Wirtingers Calculus extend include kernel case aim present manuscript twofold endeavor provide rigorous presentation related material focusing aspect author find insightful b extends notion Wirtingers calculus general Hilbert space Reproducing Hilbert Kernel Spaces Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Aleksandrs Slivkins', 'Filip Radlinski', 'Sreenivas Gollapudi']

1005.5197v2

Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a learning-to-rank formulation that optimizes the fraction of satisfied users, with several scalable algorithms that explicitly takes document similarity and ranking context into account. Our formulation is a non-trivial common generalization of two multi-armed bandit models from the literature: "ranked bandits" (Radlinski et al., ICML 2008) and "Lipschitz bandits" (Kleinberg et al., STOC 2008). We present theoretical justifications for this approach, as well as a near-optimal algorithm. Our evaluation adds optimizations that improve empirical performance, and shows that our algorithms learn orders of magnitude more quickly than previous approaches.

Ranked bandits in metric spaces: learning optimally diverse rankings over large document collections

http://arxiv.org/pdf/1005.5197v2

Title Ranked bandit metric space learning optimally diverse ranking large document collection Summary learning rank research assumed utility different document independent result learned ranking function return redundant result approach avoid rather unsatisfyingly lacked theoretical foundation scale present learningtorank formulation optimizes fraction satisfied user several scalable algorithm explicitly take document similarity ranking context account formulation nontrivial common generalization two multiarmed bandit model literature ranked bandit Radlinski et al ICML 2008 Lipschitz bandit Kleinberg et al STOC 2008 present theoretical justification approach well nearoptimal algorithm evaluation add optimization improve empirical performance show algorithm learn order magnitude quickly previous approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yucheng Low', 'Joseph Gonzalez', 'Aapo Kyrola', 'Danny Bickson', 'Carlos Guestrin', 'Joseph M. Hellerstein']

1006.4990v1

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.

GraphLab: A New Framework for Parallel Machine Learning

http://arxiv.org/pdf/1006.4990v1

Title GraphLab New Framework Parallel Machine Learning Summary Designing implementing efficient provably correct parallel machine learning ML algorithm challenging Existing highlevel parallel abstraction like MapReduce insufficiently expressive lowlevel tool like MPI Pthreads leave ML expert repeatedly solving design challenge targeting common pattern ML developed GraphLab improves upon abstraction like MapReduce compactly expressing asynchronous iterative algorithm sparse computational dependency ensuring data consistency achieving high degree parallel performance demonstrate expressiveness GraphLab framework designing implementing parallel version belief propagation Gibbs sampling CoEM Lasso Compressed Sensing show using GraphLab achieve excellent parallel performance large scale realworld problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Madjid Khalilian', 'Norwati Mustapha']

1006.5261v1

Very large databases are required to store massive amounts of data that are continuously inserted and queried. Analyzing huge data sets and extracting valuable pattern in many applications are interesting for researchers. We can identify two main groups of techniques for huge data bases mining. One group refers to streaming data and applies mining techniques whereas second group attempts to solve this problem directly with efficient algorithms. Recently many researchers have focused on data stream as an efficient strategy against huge data base mining instead of mining on entire data base. The main problem in data stream mining means evolving data is more difficult to detect in this techniques therefore unsupervised methods should be applied. However, clustering techniques can lead us to discover hidden information. In this survey, we try to clarify: first, the different problem definitions related to data stream clustering in general; second, the specific difficulties encountered in this field of research; third, the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and how several prominent solutions tackle different problems. Index Terms- Data Stream, Clustering, K-Means, Concept drift

Data Stream Clustering: Challenges and Issues

http://arxiv.org/pdf/1006.5261v1

Title Data Stream Clustering Challenges Issues Summary large database required store massive amount data continuously inserted queried Analyzing huge data set extracting valuable pattern many application interesting researcher identify two main group technique huge data base mining One group refers streaming data applies mining technique whereas second group attempt solve problem directly efficient algorithm Recently many researcher focused data stream efficient strategy huge data base mining instead mining entire data base main problem data stream mining mean evolving data difficult detect technique therefore unsupervised method applied However clustering technique lead u discover hidden information survey try clarify first different problem definition related data stream clustering general second specific difficulty encountered field research third varying assumption heuristic intuition forming basis different approach several prominent solution tackle different problem Index Terms Data Stream Clustering KMeans Concept drift Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dhoha Almazro', 'Ghadeer Shahatah', 'Lamia Albdulkarim', 'Mona Kherees', 'Romy Martinez', 'William Nzoukou']

1006.5278v4

Recommender systems apply data mining techniques and prediction algorithms to predict users' interest on information, products and services among the tremendous amount of available items. The vast growth of information on the Internet as well as number of visitors to websites add some key challenges to recommender systems. These are: producing accurate recommendation, handling many recommendations efficiently and coping with the vast growth of number of participants in the system. Therefore, new recommender system technologies are needed that can quickly produce high quality recommendations even for huge data sets. To address these issues we have explored several collaborative filtering techniques such as the item based approach, which identify relationship between items and indirectly compute recommendations for users based on these relationships. The user based approach was also studied, it identifies relationships between users of similar tastes and computes recommendations based on these relationships. In this paper, we introduce the topic of recommender system. It provides ways to evaluate efficiency, scalability and accuracy of recommender system. The paper also analyzes different algorithms of user based and item based techniques for recommendation generation. Moreover, a simple experiment was conducted using a data mining application -Weka- to apply data mining algorithms to recommender system. We conclude by proposing our approach that might enhance the quality of recommender systems.

A Survey Paper on Recommender Systems

http://arxiv.org/pdf/1006.5278v4

Title Survey Paper Recommender Systems Summary Recommender system apply data mining technique prediction algorithm predict user interest information product service among tremendous amount available item vast growth information Internet well number visitor website add key challenge recommender system producing accurate recommendation handling many recommendation efficiently coping vast growth number participant system Therefore new recommender system technology needed quickly produce high quality recommendation even huge data set address issue explored several collaborative filtering technique item based approach identify relationship item indirectly compute recommendation user based relationship user based approach also studied identifies relationship user similar taste computes recommendation based relationship paper introduce topic recommender system provides way evaluate efficiency scalability accuracy recommender system paper also analyzes different algorithm user based item based technique recommendation generation Moreover simple experiment conducted using data mining application Weka apply data mining algorithm recommender system conclude proposing approach might enhance quality recommender system Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jérôme Kunegis', 'Ernesto W. De Luca', 'Sahin Albayrak']

1006.5367v1

We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positive-semidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document--feature networks and other types of bipartite networks.

The Link Prediction Problem in Bipartite Networks

http://arxiv.org/pdf/1006.5367v1

Title Link Prediction Problem Bipartite Networks Summary define study link prediction problem bipartite network specializing general link prediction algorithm bipartite case graph link prediction function two vertex denotes similarity proximity vertex Common link prediction function general graph defined using path length two two node Since bipartite graph adjacency vertex connected path odd length function apply bipartite graph Instead certain class graph kernel spectral transformation kernel generalized bipartite graph positivesemidefinite kernel constraint relaxed generalization realized odd component underlying spectral transformation construction lead several new link prediction pseudokernels matrix hyperbolic sine examine rating graph authorship graph folksonomies documentfeature network type bipartite network Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Laurent El Ghaoui', 'Vivian Viallon', 'Tarek Rabbani']

1009.3515v2

We investigate fast methods that allow to quickly eliminate variables (features) in supervised learning problems involving a convex loss function and a $l_1$-norm penalty, leading to a potentially substantial reduction in the number of variables prior to running the supervised learning algorithm. The methods are not heuristic: they only eliminate features that are {\em guaranteed} to be absent after solving the learning problem. Our framework applies to a large class of problems, including support vector machine classification, logistic regression and least-squares. The complexity of the feature elimination step is negligible compared to the typical computational effort involved in the sparse supervised learning problem: it grows linearly with the number of features times the number of examples, with much better count if data is sparse. We apply our method to data sets arising in text classification and observe a dramatic reduction of the dimensionality, hence in computational effort required to solve the learning problem, especially when very sparse classifiers are sought. Our method allows to immediately extend the scope of existing algorithms, allowing us to run them on data sets of sizes that were out of their reach before.

Safe Feature Elimination in Sparse Supervised Learning

http://arxiv.org/pdf/1009.3515v2

Title Safe Feature Elimination Sparse Supervised Learning Summary investigate fast method allow quickly eliminate variable feature supervised learning problem involving convex loss function l1norm penalty leading potentially substantial reduction number variable prior running supervised learning algorithm method heuristic eliminate feature em guaranteed absent solving learning problem framework applies large class problem including support vector machine classification logistic regression leastsquares complexity feature elimination step negligible compared typical computational effort involved sparse supervised learning problem grows linearly number feature time number example much better count data sparse apply method data set arising text classification observe dramatic reduction dimensionality hence computational effort required solve learning problem especially sparse classifier sought method allows immediately extend scope existing algorithm allowing u run data set size reach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Konstantin Biatov']

1009.4719v1

This paper describes an effective unsupervised speaker indexing approach. We suggest a two stage algorithm to speed-up the state-of-the-art algorithm based on the Bayesian Information Criterion (BIC). In the first stage of the merging process a computationally cheap method based on the vector quantization (VQ) is used. Then in the second stage a more computational expensive technique based on the BIC is applied. In the speaker indexing task a turning parameter or a threshold is used. We suggest an on-line procedure to define the value of a turning parameter without using development data. The results are evaluated using 10 hours of audio data.

A Fast Audio Clustering Using Vector Quantization and Second Order Statistics

http://arxiv.org/pdf/1009.4719v1

Title Fast Audio Clustering Using Vector Quantization Second Order Statistics Summary paper describes effective unsupervised speaker indexing approach suggest two stage algorithm speedup stateoftheart algorithm based Bayesian Information Criterion BIC first stage merging process computationally cheap method based vector quantization VQ used second stage computational expensive technique based BIC applied speaker indexing task turning parameter threshold used suggest online procedure define value turning parameter without using development data result evaluated using 10 hour audio data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['S. M. Kamruzzaman', 'A. N. M. Rezaul Karim', 'Md. Saiful Islam', 'Md. Emdadul Haque']

1009.4972v1

Speech recognition and speaker identification are important for authentication and verification in security purpose, but they are difficult to achieve. Speaker identification methods can be divided into text-independent and text-dependent. This paper presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM). In this work, melfrequency cepstrum coefficients (MFCCs) and their statistical distribution properties are used as features, which will be inputs to the neural network. This work firstly used sequential minimum optimization (SMO) learning technique for SVM that improve performance over traditional techniques Chunking, Osuna. The cepstrum coefficients representing the speaker characteristics of a speech segment are computed by nonlinear filter bank analysis and discrete cosine transform. The speaker identification ability and convergence speed of the SVMs are investigated for different combinations of features. Extensive experimental results on several samples show the effectiveness of the proposed approach.

Speaker Identification using MFCC-Domain Support Vector Machine

http://arxiv.org/pdf/1009.4972v1

Title Speaker Identification using MFCCDomain Support Vector Machine Summary Speech recognition speaker identification important authentication verification security purpose difficult achieve Speaker identification method divided textindependent textdependent paper present technique textdependent speaker identification using MFCCdomain support vector machine SVM work melfrequency cepstrum coefficient MFCCs statistical distribution property used feature input neural network work firstly used sequential minimum optimization SMO learning technique SVM improve performance traditional technique Chunking Osuna cepstrum coefficient representing speaker characteristic speech segment computed nonlinear filter bank analysis discrete cosine transform speaker identification ability convergence speed SVMs investigated different combination feature Extensive experimental result several sample show effectiveness proposed approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Matthew D. Hoffman']

1009.5761v1

In certain applications it is useful to fit multinomial distributions to observed data with a penalty term that encourages sparsity. For example, in probabilistic latent audio source decomposition one may wish to encode the assumption that only a few latent sources are active at any given time. The standard heuristic of applying an L1 penalty is not an option when fitting the parameters to a multinomial distribution, which are constrained to sum to 1. An alternative is to use a penalty term that encourages low-entropy solutions, which corresponds to maximum a posteriori (MAP) parameter estimation with an entropic prior. The lack of conjugacy between the entropic prior and the multinomial distribution complicates this approach. In this report I propose a simple iterative algorithm for MAP estimation of multinomial distributions with sparsity-inducing entropic priors.

Approximate Maximum A Posteriori Inference with Entropic Priors

http://arxiv.org/pdf/1009.5761v1

Title Approximate Maximum Posteriori Inference Entropic Priors Summary certain application useful fit multinomial distribution observed data penalty term encourages sparsity example probabilistic latent audio source decomposition one may wish encode assumption latent source active given time standard heuristic applying L1 penalty option fitting parameter multinomial distribution constrained sum 1 alternative use penalty term encourages lowentropy solution corresponds maximum posteriori MAP parameter estimation entropic prior lack conjugacy entropic prior multinomial distribution complicates approach report propose simple iterative algorithm MAP estimation multinomial distribution sparsityinducing entropic prior Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sudipto Guha', 'Kamesh Munagala', 'Martin Pal']

1011.1161v3

In this paper we initiate the study of optimization of bandit type problems in scenarios where the feedback of a play is not immediately known. This arises naturally in allocation problems which have been studied extensively in the literature, albeit in the absence of delays in the feedback. We study this problem in the Bayesian setting. In presence of delays, no solution with provable guarantees is known to exist with sub-exponential running time. We show that bandit problems with delayed feedback that arise in allocation settings can be forced to have significant structure, with a slight loss in optimality. This structure gives us the ability to reason about the relationship of single arm policies to the entangled optimum policy, and eventually leads to a O(1) approximation for a significantly general class of priors. The structural insights we develop are of key interest and carry over to the setting where the feedback of an action is available instantaneously, and we improve all previous results in this setting as well.

Multiarmed Bandit Problems with Delayed Feedback

http://arxiv.org/pdf/1011.1161v3

Title Multiarmed Bandit Problems Delayed Feedback Summary paper initiate study optimization bandit type problem scenario feedback play immediately known arises naturally allocation problem studied extensively literature albeit absence delay feedback study problem Bayesian setting presence delay solution provable guarantee known exist subexponential running time show bandit problem delayed feedback arise allocation setting forced significant structure slight loss optimality structure give u ability reason relationship single arm policy entangled optimum policy eventually lead O1 approximation significantly general class prior structural insight develop key interest carry setting feedback action available instantaneously improve previous result setting well Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jacob Abernethy', 'Peter L. Bartlett', 'Elad Hazan']

1011.1936v1

We consider the celebrated Blackwell Approachability Theorem for two-player games with vector payoffs. We show that Blackwell's result is equivalent, via efficient reductions, to the existence of "no-regret" algorithms for Online Linear Optimization. Indeed, we show that any algorithm for one such problem can be efficiently converted into an algorithm for the other. We provide a useful application of this reduction: the first efficient algorithm for calibrated forecasting.

Blackwell Approachability and Low-Regret Learning are Equivalent

http://arxiv.org/pdf/1011.1936v1

Title Blackwell Approachability LowRegret Learning Equivalent Summary consider celebrated Blackwell Approachability Theorem twoplayer game vector payoff show Blackwells result equivalent via efficient reduction existence noregret algorithm Online Linear Optimization Indeed show algorithm one problem efficiently converted algorithm provide useful application reduction first efficient algorithm calibrated forecasting Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ofer Dekel', 'Ran Gilad-Bachrach', 'Ohad Shamir', 'Lin Xiao']

1012.1370v1

The standard model of online prediction deals with serial processing of inputs by a single processor. However, in large-scale online prediction problems, where inputs arrive at a high rate, an increasingly common necessity is to distribute the computation across several processors. A non-trivial challenge is to design distributed algorithms for online prediction, which maintain good regret guarantees. In \cite{DMB}, we presented the DMB algorithm, which is a generic framework to convert any serial gradient-based online prediction algorithm into a distributed algorithm. Moreover, its regret guarantee is asymptotically optimal for smooth convex loss functions and stochastic inputs. On the flip side, it is fragile to many types of failures that are common in distributed environments. In this companion paper, we present variants of the DMB algorithm, which are resilient to many types of network failures, and tolerant to varying performance of the computing nodes.

Robust Distributed Online Prediction

http://arxiv.org/pdf/1012.1370v1

Title Robust Distributed Online Prediction Summary standard model online prediction deal serial processing input single processor However largescale online prediction problem input arrive high rate increasingly common necessity distribute computation across several processor nontrivial challenge design distributed algorithm online prediction maintain good regret guarantee citeDMB presented DMB algorithm generic framework convert serial gradientbased online prediction algorithm distributed algorithm Moreover regret guarantee asymptotically optimal smooth convex loss function stochastic input flip side fragile many type failure common distributed environment companion paper present variant DMB algorithm resilient many type network failure tolerant varying performance computing node Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jaydip Sen', 'P. Balamuralidhar', 'M. Girish Chandra', 'Harihara S. G.', 'Harish Reddy']

1012.2514v1

In a dynamic heterogeneous environment, such as pervasive and ubiquitous computing, context-aware adaptation is a key concept to meet the varying requirements of different users. Connectivity is an important context source that can be utilized for optimal management of diverse networking resources. Application QoS (Quality of service) is another important issue that should be taken into consideration for design of a context-aware system. This paper presents connectivity from the view point of context awareness, identifies various relevant raw connectivity contexts, and discusses how high-level context information can be abstracted from the raw context information. Further, rich context information is utilized in various policy representation with respect to user profile and preference, application characteristics, device capability, and network QoS conditions. Finally, a context-aware end-to-end evaluation algorithm is presented for adaptive connectivity management in a multi-access wireless network. Unlike the currently existing algorithms, the proposed algorithm takes into account user QoS parameters, and therefore, it is more practical.

Context Aware End-to-End Connectivity Management

http://arxiv.org/pdf/1012.2514v1

Title Context Aware EndtoEnd Connectivity Management Summary dynamic heterogeneous environment pervasive ubiquitous computing contextaware adaptation key concept meet varying requirement different user Connectivity important context source utilized optimal management diverse networking resource Application QoS Quality service another important issue taken consideration design contextaware system paper present connectivity view point context awareness identifies various relevant raw connectivity context discus highlevel context information abstracted raw context information rich context information utilized various policy representation respect user profile preference application characteristic device capability network QoS condition Finally contextaware endtoend evaluation algorithm presented adaptive connectivity management multiaccess wireless network Unlike currently existing algorithm proposed algorithm take account user QoS parameter therefore practical Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yuesheng Xu', 'Haizhang Zhang', 'Qinghui Zhang']

1102.1324v1

This paper studies the construction of a refinement kernel for a given operator-valued reproducing kernel such that the vector-valued reproducing kernel Hilbert space of the refinement kernel contains that of the given one as a subspace. The study is motivated from the need of updating the current operator-valued reproducing kernel in multi-task learning when underfitting or overfitting occurs. Numerical simulations confirm that the established refinement kernel method is able to meet this need. Various characterizations are provided based on feature maps and vector-valued integral representations of operator-valued reproducing kernels. Concrete examples of refining translation invariant and finite Hilbert-Schmidt operator-valued reproducing kernels are provided. Other examples include refinement of Hessian of scalar-valued translation-invariant kernels and transformation kernels. Existence and properties of operator-valued reproducing kernels preserved during the refinement process are also investigated.

Refinement of Operator-valued Reproducing Kernels

http://arxiv.org/pdf/1102.1324v1

Title Refinement Operatorvalued Reproducing Kernels Summary paper study construction refinement kernel given operatorvalued reproducing kernel vectorvalued reproducing kernel Hilbert space refinement kernel contains given one subspace study motivated need updating current operatorvalued reproducing kernel multitask learning underfitting overfitting occurs Numerical simulation confirm established refinement kernel method able meet need Various characterization provided based feature map vectorvalued integral representation operatorvalued reproducing kernel Concrete example refining translation invariant finite HilbertSchmidt operatorvalued reproducing kernel provided example include refinement Hessian scalarvalued translationinvariant kernel transformation kernel Existence property operatorvalued reproducing kernel preserved refinement process also investigated Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Cem Tekin', 'Mingyan Liu']

1102.3508v1

In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K finite-state discrete-time Markov chains (arms) with unknown state spaces and statistics. At each time step the player can play M arms. The objective of the user is to decide for each step which M of the K arms to play over a sequence of trials so as to maximize its long term reward. The restless multiarmed bandit is particularly relevant to the application of opportunistic spectrum access (OSA), where a (secondary) user has access to a set of K channels, each of time-varying condition as a result of random fading and/or certain primary users' activities.

Online Learning of Rested and Restless Bandits

http://arxiv.org/pdf/1102.3508v1

Title Online Learning Rested Restless Bandits Summary paper study online learning problem involving rested restless multiarmed bandit multiple play system consists single playeruser set K finitestate discretetime Markov chain arm unknown state space statistic time step player play arm objective user decide step K arm play sequence trial maximize long term reward restless multiarmed bandit particularly relevant application opportunistic spectrum access OSA secondary user access set K channel timevarying condition result random fading andor certain primary user activity Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Manas A. Pathak', 'Mehrbod Sharifi', 'Bhiksha Raj']

1102.4021v2

Email is a private medium of communication, and the inherent privacy constraints form a major obstacle in developing effective spam filtering methods which require access to a large amount of email data belonging to multiple users. To mitigate this problem, we envision a privacy preserving spam filtering system, where the server is able to train and evaluate a logistic regression based spam classifier on the combined email data of all users without being able to observe any emails using primitives such as homomorphic encryption and randomization. We analyze the protocols for correctness and security, and perform experiments of a prototype system on a large scale spam filtering task. State of the art spam filters often use character n-grams as features which result in large sparse data representation, which is not feasible to be used directly with our training and evaluation protocols. We explore various data independent dimensionality reduction which decrease the running time of the protocol making it feasible to use in practice while achieving high accuracy.

Privacy Preserving Spam Filtering

http://arxiv.org/pdf/1102.4021v2

Title Privacy Preserving Spam Filtering Summary Email private medium communication inherent privacy constraint form major obstacle developing effective spam filtering method require access large amount email data belonging multiple user mitigate problem envision privacy preserving spam filtering system server able train evaluate logistic regression based spam classifier combined email data user without able observe email using primitive homomorphic encryption randomization analyze protocol correctness security perform experiment prototype system large scale spam filtering task State art spam filter often use character ngrams feature result large sparse data representation feasible used directly training evaluation protocol explore various data independent dimensionality reduction decrease running time protocol making feasible use practice achieving high accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Vincent Gripon', 'Claude Berrou']

1102.4240v1

Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages, much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory.

Sparse neural networks with large learning diversity

http://arxiv.org/pdf/1102.4240v1

Title Sparse neural network large learning diversity Summary Coded recurrent neural network three level sparsity introduced first level related size message much smaller number available neuron second one provided particular coding rule acting local constraint neural activity third one characteristic low final connection density network learning phase Though proposed network simple since based binary neuron binary connection able learn large number message recall even presence strong erasure performance network assessed classifier associative memory Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Arvind Narayanan', 'Elaine Shi', 'Benjamin I. P. Rubinstein']

1102.4374v1

This paper describes the winning entry to the IJCNN 2011 Social Network Challenge run by Kaggle.com. The goal of the contest was to promote research on real-world link prediction, and the dataset was a graph obtained by crawling the popular Flickr social photo sharing website, with user identities scrubbed. By de-anonymizing much of the competition test set using our own Flickr crawl, we were able to effectively game the competition. Our attack represents a new application of de-anonymization to gaming machine learning contests, suggesting changes in how future competitions should be run. We introduce a new simulated annealing-based weighted graph matching algorithm for the seeding step of de-anonymization. We also show how to combine de-anonymization with link prediction---the latter is required to achieve good performance on the portion of the test set not de-anonymized---for example by training the predictor on the de-anonymized portion of the test set, and combining probabilistic predictions from de-anonymization and link prediction.

Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge

http://arxiv.org/pdf/1102.4374v1

Title Link Prediction Deanonymization Kaggle Social Network Challenge Summary paper describes winning entry IJCNN 2011 Social Network Challenge run Kagglecom goal contest promote research realworld link prediction dataset graph obtained crawling popular Flickr social photo sharing website user identity scrubbed deanonymizing much competition test set using Flickr crawl able effectively game competition attack represents new application deanonymization gaming machine learning contest suggesting change future competition run introduce new simulated annealingbased weighted graph matching algorithm seeding step deanonymization also show combine deanonymization link predictionthe latter required achieve good performance portion test set deanonymizedfor example training predictor deanonymized portion test set combining probabilistic prediction deanonymization link prediction Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Radim Řeh{ů}řek']

1102.5597v1

With the explosion of the size of digital dataset, the limiting factor for decomposition algorithms is the \emph{number of passes} over the input, as the input is often stored out-of-core or even off-site. Moreover, we're only interested in algorithms that operate in \emph{constant memory} w.r.t. to the input size, so that arbitrarily large input can be processed. In this paper, we present a practical comparison of two such algorithms: a distributed method that operates in a single pass over the input vs. a streamed two-pass stochastic algorithm. The experiments track the effect of distributed computing, oversampling and memory trade-offs on the accuracy and performance of the two algorithms. To ensure meaningful results, we choose the input to be a real dataset, namely the whole of the English Wikipedia, in the application settings of Latent Semantic Analysis.

Fast and Faster: A Comparison of Two Streamed Matrix Decomposition Algorithms

http://arxiv.org/pdf/1102.5597v1

Title Fast Faster Comparison Two Streamed Matrix Decomposition Algorithms Summary explosion size digital dataset limiting factor decomposition algorithm emphnumber pass input input often stored outofcore even offsite Moreover interested algorithm operate emphconstant memory wrt input size arbitrarily large input processed paper present practical comparison two algorithm distributed method operates single pas input v streamed twopass stochastic algorithm experiment track effect distributed computing oversampling memory tradeoff accuracy performance two algorithm ensure meaningful result choose input real dataset namely whole English Wikipedia application setting Latent Semantic Analysis Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wahiba Ben Abdessalem Karaa']

1102.5728v1

This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE) can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE's nature. As such, The occurrence of the word "President" in a text, means that this word or context may be followed by the name of a president as President "Obama". Likewise, a word preceded by the string "footballer" induces that this is the name of a footballer. NE recognition may be viewed as a classification method, where every word is assigned to a NE class, regarding the context. The aim of this study is then to identify and classify the contexts that are most relevant to recognize a NE, those which are frequently found with the NE. A learning approach using training corpus: web documents, constructed from learning examples is then suggested. Frequency representations and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.

Named Entity Recognition Using Web Document Corpus

http://arxiv.org/pdf/1102.5728v1

Title Named Entity Recognition Using Web Document Corpus Summary paper introduces named entity recognition approach textual corpus Named Entity NE named location person organization date time etc characterized instance NE found text accompanied context word left right NE work mainly aim identifying context inducing NEs nature occurrence word President text mean word context may followed name president President Obama Likewise word preceded string footballer induces name footballer NE recognition may viewed classification method every word assigned NE class regarding context aim study identify classify context relevant recognize NE frequently found NE learning approach using training corpus web document constructed learning example suggested Frequency representation modified tfidf representation used calculate context weight associated context frequency learning example frequency document frequency corpus Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jeff M. Phillips', 'Suresh Venkatasubramanian']

1103.1625v2

This document reviews the definition of the kernel distance, providing a gentle introduction tailored to a reader with background in theoretical computer science, but limited exposure to technology more common to machine learning, functional analysis and geometric measure theory. The key aspect of the kernel distance developed here is its interpretation as an L_2 distance between probability measures or various shapes (e.g. point sets, curves, surfaces) embedded in a vector space (specifically an RKHS). This structure enables several elegant and efficient solutions to data analysis problems. We conclude with a glimpse into the mathematical underpinnings of this measure, highlighting its recent independent evolution in two separate fields.

A Gentle Introduction to the Kernel Distance

http://arxiv.org/pdf/1103.1625v2

Title Gentle Introduction Kernel Distance Summary document review definition kernel distance providing gentle introduction tailored reader background theoretical computer science limited exposure technology common machine learning functional analysis geometric measure theory key aspect kernel distance developed interpretation L2 distance probability measure various shape eg point set curve surface embedded vector space specifically RKHS structure enables several elegant efficient solution data analysis problem conclude glimpse mathematical underpinnings measure highlighting recent independent evolution two separate field Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hua Ouyang', 'Alexander Gray']

1105.2274v1

In this paper, we focus on the question of the extent to which online learning can benefit from distributed computing. We focus on the setting in which $N$ agents online-learn cooperatively, where each agent only has access to its own data. We propose a generic data-distributed online learning meta-algorithm. We then introduce the Distributed Weighted Majority and Distributed Online Mirror Descent algorithms, as special cases. We show, using both theoretical analysis and experiments, that compared to a single agent: given the same computation time, these distributed algorithms achieve smaller generalization errors; and given the same generalization errors, they can be $N$ times faster.

Data-Distributed Weighted Majority and Online Mirror Descent

http://arxiv.org/pdf/1105.2274v1

Title DataDistributed Weighted Majority Online Mirror Descent Summary paper focus question extent online learning benefit distributed computing focus setting N agent onlinelearn cooperatively agent access data propose generic datadistributed online learning metaalgorithm introduce Distributed Weighted Majority Distributed Online Mirror Descent algorithm special case show using theoretical analysis experiment compared single agent given computation time distributed algorithm achieve smaller generalization error given generalization error N time faster Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Nathan Keller', 'Elchanan Mossel', 'Tomer Schlank']

1105.2651v1

The entropy/influence conjecture, raised by Friedgut and Kalai in 1996, seeks to relate two different measures of concentration of the Fourier coefficients of a Boolean function. Roughly saying, it claims that if the Fourier spectrum is "smeared out", then the Fourier coefficients are concentrated on "high" levels. In this note we generalize the conjecture to biased product measures on the discrete cube, and prove a variant of the conjecture for functions with an extremely low Fourier weight on the "high" levels.

A Note on the Entropy/Influence Conjecture

http://arxiv.org/pdf/1105.2651v1

Title Note EntropyInfluence Conjecture Summary entropyinfluence conjecture raised Friedgut Kalai 1996 seek relate two different measure concentration Fourier coefficient Boolean function Roughly saying claim Fourier spectrum smeared Fourier coefficient concentrated high level note generalize conjecture biased product measure discrete cube prove variant conjecture function extremely low Fourier weight high level Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Alejandro Chinea', 'Elka Korutcheva']

1106.1113v1

Graph-based representations of images have recently acquired an important role for classification purposes within the context of machine learning approaches. The underlying idea is to consider that relevant information of an image is implicitly encoded into the relationships between more basic entities that compose by themselves the whole image. The classification problem is then reformulated in terms of an optimization problem usually solved by a gradient-based search procedure. Vario-eta through structure is an approximate second order stochastic optimization technique that achieves a good trade-off between speed of convergence and the computational effort required. However, the robustness of this technique for large scale problems has not been yet assessed. In this paper we firstly provide a theoretical justification of the assumptions made by this optimization procedure. Secondly, a complexity analysis of the algorithm is performed to prove its suitability for large scale learning problems.

Complexity Analysis of Vario-eta through Structure

http://arxiv.org/pdf/1106.1113v1

Title Complexity Analysis Varioeta Structure Summary Graphbased representation image recently acquired important role classification purpose within context machine learning approach underlying idea consider relevant information image implicitly encoded relationship basic entity compose whole image classification problem reformulated term optimization problem usually solved gradientbased search procedure Varioeta structure approximate second order stochastic optimization technique achieves good tradeoff speed convergence computational effort required However robustness technique large scale problem yet assessed paper firstly provide theoretical justification assumption made optimization procedure Secondly complexity analysis algorithm performed prove suitability large scale learning problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Andrei N. Soklakov']

1106.2882v1

The recent crisis and the following flight to simplicity put most derivative businesses around the world under considerable pressure. We argue that the traditional modeling techniques must be extended to include product design. We propose a quantitative framework for creating products which meet the challenge of being optimal from the investors point of view while remaining relatively simple and transparent.

Learning, investments and derivatives

http://arxiv.org/pdf/1106.2882v1

Title Learning investment derivative Summary recent crisis following flight simplicity put derivative business around world considerable pressure argue traditional modeling technique must extended include product design propose quantitative framework creating product meet challenge optimal investor point view remaining relatively simple transparent Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sławomir Staworko', 'Piotr Wieczorek']

1106.3725v3

We investigate the problem of learning XML queries, path queries and tree pattern queries, from examples given by the user. A learning algorithm takes on the input a set of XML documents with nodes annotated by the user and returns a query that selects the nodes in a manner consistent with the annotation. We study two learning settings that differ with the types of annotations. In the first setting the user may only indicate required nodes that the query must return. In the second, more general, setting, the user may also indicate forbidden nodes that the query must not return. The query may or may not return any node with no annotation. We formalize what it means for a class of queries to be \emph{learnable}. One requirement is the existence of a learning algorithm that is sound i.e., always returns a query consistent with the examples given by the user. Furthermore, the learning algorithm should be complete i.e., able to produce every query with a sufficiently rich example. Other requirements involve tractability of learning and its robustness to nonessential examples. We show that the classes of simple path queries and path-subsumption-free tree queries are learnable from positive examples. The learnability of the full class of tree pattern queries (and the full class of path queries) remains an open question. We show also that adding negative examples to the picture renders the learning unfeasible. Published in ICDT 2012, Berlin.

Learning XML Twig Queries

http://arxiv.org/pdf/1106.3725v3

Title Learning XML Twig Queries Summary investigate problem learning XML query path query tree pattern query example given user learning algorithm take input set XML document node annotated user return query selects node manner consistent annotation study two learning setting differ type annotation first setting user may indicate required node query must return second general setting user may also indicate forbidden node query must return query may may return node annotation formalize mean class query emphlearnable One requirement existence learning algorithm sound ie always return query consistent example given user Furthermore learning algorithm complete ie able produce every query sufficiently rich example requirement involve tractability learning robustness nonessential example show class simple path query pathsubsumptionfree tree query learnable positive example learnability full class tree pattern query full class path query remains open question show also adding negative example picture render learning unfeasible Published ICDT 2012 Berlin Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Haizhang Zhang', 'Liang Zhao']

1106.4075v1

To help understand various reproducing kernels used in applied sciences, we investigate the inclusion relation of two reproducing kernel Hilbert spaces. Characterizations in terms of feature maps of the corresponding reproducing kernels are established. A full table of inclusion relations among widely-used translation invariant kernels is given. Concrete examples for Hilbert-Schmidt kernels are presented as well. We also discuss the preservation of such a relation under various operations of reproducing kernels. Finally, we briefly discuss the special inclusion with a norm equivalence.

On the Inclusion Relation of Reproducing Kernel Hilbert Spaces

http://arxiv.org/pdf/1106.4075v1

Title Inclusion Relation Reproducing Kernel Hilbert Spaces Summary help understand various reproducing kernel used applied science investigate inclusion relation two reproducing kernel Hilbert space Characterizations term feature map corresponding reproducing kernel established full table inclusion relation among widelyused translation invariant kernel given Concrete example HilbertSchmidt kernel presented well also discus preservation relation various operation reproducing kernel Finally briefly discus special inclusion norm equivalence Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Feng Niu', 'Benjamin Recht', 'Christopher Re', 'Stephen J. Wright']

1106.5730v2

Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented without any locking. We present an update scheme called HOGWILD! which allows processors access to shared memory with the possibility of overwriting each other's work. We show that when the associated optimization problem is sparse, meaning most gradient updates only modify small parts of the decision variable, then HOGWILD! achieves a nearly optimal rate of convergence. We demonstrate experimentally that HOGWILD! outperforms alternative schemes that use locking by an order of magnitude.

HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

http://arxiv.org/pdf/1106.5730v2

Title HOGWILD LockFree Approach Parallelizing Stochastic Gradient Descent Summary Stochastic Gradient Descent SGD popular algorithm achieve stateoftheart performance variety machine learning task Several researcher recently proposed scheme parallelize SGD require performancedestroying memory locking synchronization work aim show using novel theoretical analysis algorithm implementation SGD implemented without locking present update scheme called HOGWILD allows processor access shared memory possibility overwriting others work show associated optimization problem sparse meaning gradient update modify small part decision variable HOGWILD achieves nearly optimal rate convergence demonstrate experimentally HOGWILD outperforms alternative scheme use locking order magnitude Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jeff M. Phillips', 'Parasaran Raman', 'Suresh Venkatasubramanian']

1108.0017v1

We provide a new framework for generating multiple good quality partitions (clusterings) of a single data set. Our approach decomposes this problem into two components, generating many high-quality partitions, and then grouping these partitions to obtain k representatives. The decomposition makes the approach extremely modular and allows us to optimize various criteria that control the choice of representative partitions.

Generating a Diverse Set of High-Quality Clusterings

http://arxiv.org/pdf/1108.0017v1

Title Generating Diverse Set HighQuality Clusterings Summary provide new framework generating multiple good quality partition clustering single data set approach decomposes problem two component generating many highquality partition grouping partition obtain k representative decomposition make approach extremely modular allows u optimize various criterion control choice representative partition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yao Wu', 'Qiang Yan', 'Danny Bickson', 'Yucheng Low', 'Qing Yang']

1108.2580v2

This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two main challenges: the unique item taxonomy characteristics as well as the large data set size.To handle the item taxonomy, we present a novel method called Matrix Factorization Item Taxonomy Regularization (MFITR). MFITR obtained the 2nd best prediction result out of more then ten implemented algorithms. For rapidly computing multiple solutions of various algorithms, we have implemented an open source parallel collaborative filtering library on top of the GraphLab machine learning framework. We report some preliminary performance results obtained using the BlackLight supercomputer.

Efficient Multicore Collaborative Filtering

http://arxiv.org/pdf/1108.2580v2

Title Efficient Multicore Collaborative Filtering Summary paper describes solution method taken LeBuSiShu team track1 ACM KDD CUP 2011 contest resulting 5th place identified two main challenge unique item taxonomy characteristic well large data set sizeTo handle item taxonomy present novel method called Matrix Factorization Item Taxonomy Regularization MFITR MFITR obtained 2nd best prediction result ten implemented algorithm rapidly computing multiple solution various algorithm implemented open source parallel collaborative filtering library top GraphLab machine learning framework report preliminary performance result obtained using BlackLight supercomputer Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Meir Perez', 'Tshilidzi Marwala']

1108.4545v1

The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed to rank genes in order of differential expression, based on expression data generated in a microarray experiment. This paper examines the effectiveness of the FGF for feature selection using various classification architectures. The FGF is compared to three of the most common gene ranking algorithms: t-test, Wilcoxon test and ROC curve analysis. Four classification schemes are used to compare the performance of the FGF vis-a-vis the standard approaches: K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and Artificial Neural Network (ANN). A nested stratified Leave-One-Out Cross Validation scheme is used to identify the optimal number top ranking genes, as well as the optimal classifier parameters. Two microarray data sets are used for the comparison: a prostate cancer data set and a lymphoma data set.

The fuzzy gene filter: A classifier performance assesment

http://arxiv.org/pdf/1108.4545v1

Title fuzzy gene filter classifier performance assesment Summary Fuzzy Gene Filter FGF optimised Fuzzy Inference System designed rank gene order differential expression based expression data generated microarray experiment paper examines effectiveness FGF feature selection using various classification architecture FGF compared three common gene ranking algorithm ttest Wilcoxon test ROC curve analysis Four classification scheme used compare performance FGF visavis standard approach K Nearest Neighbour KNN Support Vector Machine SVM Naive Bayesian Classifier NBC Artificial Neural Network ANN nested stratified LeaveOneOut Cross Validation scheme used identify optimal number top ranking gene well optimal classifier parameter Two microarray data set used comparison prostate cancer data set lymphoma data set Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Mlungisi Duma', 'Bhekisipho Twala', 'Tshilidzi Marwala']

1108.4551v1

The Ripper algorithm is designed to generate rule sets for large datasets with many features. However, it was shown that the algorithm struggles with classification performance in the presence of missing data. The algorithm struggles to classify instances when the quality of the data deteriorates as a result of increasing missing data. In this paper, a feature selection technique is used to help improve the classification performance of the Ripper model. Principal component analysis and evidence automatic relevance determination techniques are used to improve the performance. A comparison is done to see which technique helps the algorithm improve the most. Training datasets with completely observable data were used to construct the model and testing datasets with missing values were used for measuring accuracy. The results showed that principal component analysis is a better feature selection for the Ripper in improving the classification performance.

Improving the performance of the ripper in insurance risk classification : A comparitive study using feature selection

http://arxiv.org/pdf/1108.4551v1

Title Improving performance ripper insurance risk classification comparitive study using feature selection Summary Ripper algorithm designed generate rule set large datasets many feature However shown algorithm struggle classification performance presence missing data algorithm struggle classify instance quality data deteriorates result increasing missing data paper feature selection technique used help improve classification performance Ripper model Principal component analysis evidence automatic relevance determination technique used improve performance comparison done see technique help algorithm improve Training datasets completely observable data used construct model testing datasets missing value used measuring accuracy result showed principal component analysis better feature selection Ripper improving classification performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Massimo Melucci']

1108.5784v1

The Probability Ranking Principle states that the document set with the highest values of probability of relevance optimizes information retrieval effectiveness given the probabilities are estimated as accurately as possible. The key point of the principle is the separation of the document set into two subsets with a given level of fallout and with the highest recall. The paper introduces the separation between two vector subspaces and shows that the separation yields a more effective performance than the optimal separation into subsets with the same available evidence, the performance being measured with recall and fallout. The result is proved mathematically and exemplified experimentally.

Probability Ranking in Vector Spaces

http://arxiv.org/pdf/1108.5784v1

Title Probability Ranking Vector Spaces Summary Probability Ranking Principle state document set highest value probability relevance optimizes information retrieval effectiveness given probability estimated accurately possible key point principle separation document set two subset given level fallout highest recall paper introduces separation two vector subspace show separation yield effective performance optimal separation subset available evidence performance measured recall fallout result proved mathematically exemplified experimentally Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dean Foster', 'Alexander Rakhlin']

1108.6088v1

We present an algorithm which attains O(\sqrt{T}) internal (and thus external) regret for finite games with partial monitoring under the local observability condition. Recently, this condition has been shown by (Bartok, Pal, and Szepesvari, 2011) to imply the O(\sqrt{T}) rate for partial monitoring games against an i.i.d. opponent, and the authors conjectured that the same holds for non-stochastic adversaries. Our result is in the affirmative, and it completes the characterization of possible rates for finite partial-monitoring games, an open question stated by (Cesa-Bianchi, Lugosi, and Stoltz, 2006). Our regret guarantees also hold for the more general model of partial monitoring with random signals.

No Internal Regret via Neighborhood Watch

http://arxiv.org/pdf/1108.6088v1

Title Internal Regret via Neighborhood Watch Summary present algorithm attains OsqrtT internal thus external regret finite game partial monitoring local observability condition Recently condition shown Bartok Pal Szepesvari 2011 imply OsqrtT rate partial monitoring game iid opponent author conjectured hold nonstochastic adversary result affirmative completes characterization possible rate finite partialmonitoring game open question stated CesaBianchi Lugosi Stoltz 2006 regret guarantee also hold general model partial monitoring random signal Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Zenglin Xu', 'Feng Yan', 'Yuan', 'Qi']

1108.6296v2

Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches---such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)---amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entities, (ii) various data types (e.g. missing data and binary data), and (iii) noisy observations and outliers. To address these issues, we propose tensor-variate latent nonparametric Bayesian models, coupled with efficient inference methods, for multiway data analysis. We name these models InfTucker. Using these InfTucker, we conduct Tucker decomposition in an infinite feature space. Unlike classical tensor decomposition models, our new approaches handle both continuous and binary data in a probabilistic framework. Unlike previous Bayesian models on matrices and tensors, our models are based on latent Gaussian or $t$ processes with nonlinear covariance functions. To efficiently learn the InfTucker from data, we develop a variational inference technique on tensors. Compared with classical implementation, the new technique reduces both time and space complexities by several orders of magnitude. Our experimental results on chemometrics and social network datasets demonstrate that our new models achieved significantly higher prediction accuracy than the most state-of-art tensor decomposition

Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis

http://arxiv.org/pdf/1108.6296v2

Title Infinite Tucker Decomposition Nonparametric Bayesian Models Multiway Data Analysis Summary Tensor decomposition powerful computational tool multiway data analysis Many popular tensor decomposition approachessuch Tucker decomposition CANDECOMPPARAFAC CPamount multilinear factorization insufficient model complex interaction data entity ii various data type eg missing data binary data iii noisy observation outlier address issue propose tensorvariate latent nonparametric Bayesian model coupled efficient inference method multiway data analysis name model InfTucker Using InfTucker conduct Tucker decomposition infinite feature space Unlike classical tensor decomposition model new approach handle continuous binary data probabilistic framework Unlike previous Bayesian model matrix tensor model based latent Gaussian process nonlinear covariance function efficiently learn InfTucker data develop variational inference technique tensor Compared classical implementation new technique reduces time space complexity several order magnitude experimental result chemometrics social network datasets demonstrate new model achieved significantly higher prediction accuracy stateofart tensor decomposition Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Matus Telgarsky']

1110.1514v1

This manuscript investigates the relationship between Blackwell Approachability, a stochastic vector-valued repeated game, and minimax theory, a single-play scalar-valued scenario. First, it is established in a general setting --- one not permitting invocation of minimax theory --- that Blackwell's Approachability Theorem and its generalization due to Hou are still valid. Second, minimax structure grants a result in the spirit of Blackwell's weak-approachability conjecture, later resolved by Vieille, that any set is either approachable by one player, or avoidable by the opponent. This analysis also reveals a strategy for the opponent.

Blackwell Approachability and Minimax Theory

http://arxiv.org/pdf/1110.1514v1

Title Blackwell Approachability Minimax Theory Summary manuscript investigates relationship Blackwell Approachability stochastic vectorvalued repeated game minimax theory singleplay scalarvalued scenario First established general setting one permitting invocation minimax theory Blackwells Approachability Theorem generalization due Hou still valid Second minimax structure grant result spirit Blackwells weakapproachability conjecture later resolved Vieille set either approachable one player avoidable opponent analysis also reveals strategy opponent Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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-0.004982744809240103, 0.07099910825490952, -0.018321163952350616, -0.027803560718894005]

['G. Kesidis', 'A. Kurve']

1110.1781v1

We consider unsupervised crowdsourcing performance based on the model wherein the responses of end-users are essentially rated according to how their responses correlate with the majority of other responses to the same subtasks/questions. In one setting, we consider an independent sequence of identically distributed crowdsourcing assignments (meta-tasks), while in the other we consider a single assignment with a large number of component subtasks. Both problems yield intuitive results in which the overall reliability of the crowd is a factor.

A Study of Unsupervised Adaptive Crowdsourcing

http://arxiv.org/pdf/1110.1781v1

Title Study Unsupervised Adaptive Crowdsourcing Summary consider unsupervised crowdsourcing performance based model wherein response endusers essentially rated according response correlate majority response subtasksquestions one setting consider independent sequence identically distributed crowdsourcing assignment metatasks consider single assignment large number component subtasks problem yield intuitive result overall reliability crowd factor Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Dip Narayan Ray', 'Somajyoti Majumder', 'Sumit Mukhopadhyay']

1110.1796v1

The use of mobile robots is being popular over the world mainly for autonomous explorations in hazardous/ toxic or unknown environments. This exploration will be more effective and efficient if the explorations in unknown environment can be aided with the learning from past experiences. Currently reinforcement learning is getting more acceptances for implementing learning in robots from the system-environment interactions. This learning can be implemented using the concept of both single-agent and multiagent. This paper describes such a multiagent approach for implementing a type of reinforcement learning using a priority based behaviour-based architecture. This proposed methodology has been successfully tested in both indoor and outdoor environments.

A Behavior-based Approach for Multi-agent Q-learning for Autonomous Exploration

http://arxiv.org/pdf/1110.1796v1

Title Behaviorbased Approach Multiagent Qlearning Autonomous Exploration Summary use mobile robot popular world mainly autonomous exploration hazardous toxic unknown environment exploration effective efficient exploration unknown environment aided learning past experience Currently reinforcement learning getting acceptance implementing learning robot systemenvironment interaction learning implemented using concept singleagent multiagent paper describes multiagent approach implementing type reinforcement learning using priority based behaviourbased architecture proposed methodology successfully tested indoor outdoor environment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ohad Shamir']

1110.2392v2

We provide a variant of Azuma's concentration inequality for martingales, in which the standard boundedness requirement is replaced by the milder requirement of a subgaussian tail.

A Variant of Azuma's Inequality for Martingales with Subgaussian Tails

http://arxiv.org/pdf/1110.2392v2

Title Variant Azumas Inequality Martingales Subgaussian Tails Summary provide variant Azumas concentration inequality martingale standard boundedness requirement replaced milder requirement subgaussian tail Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Parul Agarwal', 'M. Afshar Alam', 'Ranjit Biswas']

1110.2610v1

Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools which are readily available and support functions which ease the programming. Once algorithms have been implemented, they also need to be tested for its validity. There exist several validation indexes for testing the performance and accuracy which have also been discussed here.

Issues,Challenges and Tools of Clustering Algorithms

http://arxiv.org/pdf/1110.2610v1

Title IssuesChallenges Tools Clustering Algorithms Summary Clustering unsupervised technique Data Mining mean grouping similar object together separating dissimilar one object data set assigned class label clustering process using distance measure paper captured problem faced real clustering algorithm implemented also considers extensively used tool readily available support function ease programming algorithm implemented also need tested validity exist several validation index testing performance accuracy also discussed Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['K. Usha Rani']

1110.2626v1

One of the important techniques of Data mining is Classification. Many real world problems in various fields such as business, science, industry and medicine can be solved by using classification approach. Neural Networks have emerged as an important tool for classification. The advantages of Neural Networks helps for efficient classification of given data. In this study a Heart diseases dataset is analyzed using Neural Network approach. To increase the efficiency of the classification process parallel approach is also adopted in the training phase.

Analysis of Heart Diseases Dataset using Neural Network Approach

http://arxiv.org/pdf/1110.2626v1

Title Analysis Heart Diseases Dataset using Neural Network Approach Summary One important technique Data mining Classification Many real world problem various field business science industry medicine solved using classification approach Neural Networks emerged important tool classification advantage Neural Networks help efficient classification given data study Heart disease dataset analyzed using Neural Network approach increase efficiency classification process parallel approach also adopted training phase Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Daniel Hsu', 'Sham M. Kakade', 'Tong Zhang']

1110.2842v1

We prove an exponential probability tail inequality for positive semidefinite quadratic forms in a subgaussian random vector. The bound is analogous to one that holds when the vector has independent Gaussian entries.

A tail inequality for quadratic forms of subgaussian random vectors

http://arxiv.org/pdf/1110.2842v1

Title tail inequality quadratic form subgaussian random vector Summary prove exponential probability tail inequality positive semidefinite quadratic form subgaussian random vector bound analogous one hold vector independent Gaussian entry Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Christos Boutsidis', 'Anastasios Zouzias', 'Michael W. Mahoney', 'Petros Drineas']

1110.2897v3

We study the topic of dimensionality reduction for $k$-means clustering. Dimensionality reduction encompasses the union of two approaches: \emph{feature selection} and \emph{feature extraction}. A feature selection based algorithm for $k$-means clustering selects a small subset of the input features and then applies $k$-means clustering on the selected features. A feature extraction based algorithm for $k$-means clustering constructs a small set of new artificial features and then applies $k$-means clustering on the constructed features. Despite the significance of $k$-means clustering as well as the wealth of heuristic methods addressing it, provably accurate feature selection methods for $k$-means clustering are not known. On the other hand, two provably accurate feature extraction methods for $k$-means clustering are known in the literature; one is based on random projections and the other is based on the singular value decomposition (SVD). This paper makes further progress towards a better understanding of dimensionality reduction for $k$-means clustering. Namely, we present the first provably accurate feature selection method for $k$-means clustering and, in addition, we present two feature extraction methods. The first feature extraction method is based on random projections and it improves upon the existing results in terms of time complexity and number of features needed to be extracted. The second feature extraction method is based on fast approximate SVD factorizations and it also improves upon the existing results in terms of time complexity. The proposed algorithms are randomized and provide constant-factor approximation guarantees with respect to the optimal $k$-means objective value.

Randomized Dimensionality Reduction for k-means Clustering

http://arxiv.org/pdf/1110.2897v3

Title Randomized Dimensionality Reduction kmeans Clustering Summary study topic dimensionality reduction kmeans clustering Dimensionality reduction encompasses union two approach emphfeature selection emphfeature extraction feature selection based algorithm kmeans clustering selects small subset input feature applies kmeans clustering selected feature feature extraction based algorithm kmeans clustering construct small set new artificial feature applies kmeans clustering constructed feature Despite significance kmeans clustering well wealth heuristic method addressing provably accurate feature selection method kmeans clustering known hand two provably accurate feature extraction method kmeans clustering known literature one based random projection based singular value decomposition SVD paper make progress towards better understanding dimensionality reduction kmeans clustering Namely present first provably accurate feature selection method kmeans clustering addition present two feature extraction method first feature extraction method based random projection improves upon existing result term time complexity number feature needed extracted second feature extraction method based fast approximate SVD factorization also improves upon existing result term time complexity proposed algorithm randomized provide constantfactor approximation guarantee respect optimal kmeans objective value Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Peng Cheng']

1110.3001v1

We propose a first-order method for stochastic strongly convex optimization that attains $O(1/n)$ rate of convergence, analysis show that the proposed method is simple, easily to implement, and in worst case, asymptotically four times faster than its peers. We derive this method from several intuitive observations that are generalized from existing first order optimization methods.

Step size adaptation in first-order method for stochastic strongly convex programming

http://arxiv.org/pdf/1110.3001v1

Title Step size adaptation firstorder method stochastic strongly convex programming Summary propose firstorder method stochastic strongly convex optimization attains O1n rate convergence analysis show proposed method simple easily implement worst case asymptotically four time faster peer derive method several intuitive observation generalized existing first order optimization method Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['C. Staiger', 'S. Cadot', 'R. Kooter', 'M. Dittrich', 'T. Mueller', 'G. W. Klau', 'L. F. A. Wessels']

1110.3717v2

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer.

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

http://arxiv.org/pdf/1110.3717v2

Title critical evaluation network pathway based classifier outcome prediction breast cancer Summary Recently several classifier combine primary tumor data like gene expression data secondary data source proteinprotein interaction network proposed predicting outcome breast cancer approach new composite feature typically constructed aggregating expression level several gene secondary data source employed guide aggregation Although many study claim approach improve classification performance single gene classifier gain performance difficult ass stem mainly fact different breast cancer data set validation procedure employed ass performance address issue employing large cohort six breast cancer data set benchmark set performing unbiased evaluation classification accuracy different approach Contrary previous claim find composite feature classifier outperform simple single gene classifier investigate effect 1 number selected feature 2 specific gene set feature selected 3 size training set 4 heterogeneity data set performance composite feature single gene classifier Strikingly find randomization secondary data source destroys biological information source result deterioration performance composite feature classifier Finally show proper correction gene set size performed stability single gene set similar stability composite feature set Based result currently reason prefer prognostic classifier based composite feature single gene classifier predicting outcome breast cancer Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Wouter Lueks', 'Bassam Mokbel', 'Michael Biehl', 'Barbara Hammer']

1110.3917v1

The growing number of dimensionality reduction methods available for data visualization has recently inspired the development of quality assessment measures, in order to evaluate the resulting low-dimensional representation independently from a methods' inherent criteria. Several (existing) quality measures can be (re)formulated based on the so-called co-ranking matrix, which subsumes all rank errors (i.e. differences between the ranking of distances from every point to all others, comparing the low-dimensional representation to the original data). The measures are often based on the partioning of the co-ranking matrix into 4 submatrices, divided at the K-th row and column, calculating a weighted combination of the sums of each submatrix. Hence, the evaluation process typically involves plotting a graph over several (or even all possible) settings of the parameter K. Considering simple artificial examples, we argue that this parameter controls two notions at once, that need not necessarily be combined, and that the rectangular shape of submatrices is disadvantageous for an intuitive interpretation of the parameter. We debate that quality measures, as general and flexible evaluation tools, should have parameters with a direct and intuitive interpretation as to which specific error types are tolerated or penalized. Therefore, we propose to replace K with two parameters to control these notions separately, and introduce a differently shaped weighting on the co-ranking matrix. The two new parameters can then directly be interpreted as a threshold up to which rank errors are tolerated, and a threshold up to which the rank-distances are significant for the evaluation. Moreover, we propose a color representation of local quality to visually support the evaluation process for a given mapping, where every point in the mapping is colored according to its local contribution to the overall quality.

How to Evaluate Dimensionality Reduction? - Improving the Co-ranking Matrix

http://arxiv.org/pdf/1110.3917v1

Title Evaluate Dimensionality Reduction Improving Coranking Matrix Summary growing number dimensionality reduction method available data visualization recently inspired development quality assessment measure order evaluate resulting lowdimensional representation independently method inherent criterion Several existing quality measure reformulated based socalled coranking matrix subsumes rank error ie difference ranking distance every point others comparing lowdimensional representation original data measure often based partioning coranking matrix 4 submatrices divided Kth row column calculating weighted combination sum submatrix Hence evaluation process typically involves plotting graph several even possible setting parameter K Considering simple artificial example argue parameter control two notion need necessarily combined rectangular shape submatrices disadvantageous intuitive interpretation parameter debate quality measure general flexible evaluation tool parameter direct intuitive interpretation specific error type tolerated penalized Therefore propose replace K two parameter control notion separately introduce differently shaped weighting coranking matrix two new parameter directly interpreted threshold rank error tolerated threshold rankdistances significant evaluation Moreover propose color representation local quality visually support evaluation process given mapping every point mapping colored according local contribution overall quality Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Georgios C. Chasparis', 'Ari Arapostathis', 'Jeff S. Shamma']

1110.4412v1

We consider the problem of distributed convergence to efficient outcomes in coordination games through dynamics based on aspiration learning. Under aspiration learning, a player continues to play an action as long as the rewards received exceed a specified aspiration level. Here, the aspiration level is a fading memory average of past rewards, and these levels also are subject to occasional random perturbations. A player becomes dissatisfied whenever a received reward is less than the aspiration level, in which case the player experiments with a probability proportional to the degree of dissatisfaction. Our first contribution is the characterization of the asymptotic behavior of the induced Markov chain of the iterated process in terms of an equivalent finite-state Markov chain. We then characterize explicitly the behavior of the proposed aspiration learning in a generalized version of coordination games, examples of which include network formation and common-pool games. In particular, we show that in generic coordination games the frequency at which an efficient action profile is played can be made arbitrarily large. Although convergence to efficient outcomes is desirable, in several coordination games, such as common-pool games, attainability of fair outcomes, i.e., sequences of plays at which players experience highly rewarding returns with the same frequency, might also be of special interest. To this end, we demonstrate through analysis and simulations that aspiration learning also establishes fair outcomes in all symmetric coordination games, including common-pool games.

Aspiration Learning in Coordination Games

http://arxiv.org/pdf/1110.4412v1

Title Aspiration Learning Coordination Games Summary consider problem distributed convergence efficient outcome coordination game dynamic based aspiration learning aspiration learning player continues play action long reward received exceed specified aspiration level aspiration level fading memory average past reward level also subject occasional random perturbation player becomes dissatisfied whenever received reward le aspiration level case player experiment probability proportional degree dissatisfaction first contribution characterization asymptotic behavior induced Markov chain iterated process term equivalent finitestate Markov chain characterize explicitly behavior proposed aspiration learning generalized version coordination game example include network formation commonpool game particular show generic coordination game frequency efficient action profile played made arbitrarily large Although convergence efficient outcome desirable several coordination game commonpool game attainability fair outcome ie sequence play player experience highly rewarding return frequency might also special interest end demonstrate analysis simulation aspiration learning also establishes fair outcome symmetric coordination game including commonpool game Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sébastien Bubeck', 'Damien Ernst', 'Aurélien Garivier']

1110.5447v1

We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it with an algorithm based on the optimistic paradigm and the Good-Turing missing mass estimator. We show that this strategy uniformly attains the optimal discovery rate in a macroscopic limit sense, under some assumptions on the probabilistic experts. We also provide numerical experiments suggesting that this optimal behavior may still hold under weaker assumptions.

Optimal discovery with probabilistic expert advice

http://arxiv.org/pdf/1110.5447v1

Title Optimal discovery probabilistic expert advice Summary consider original problem arises issue security analysis power system name optimal discovery probabilistic expert advice address algorithm based optimistic paradigm GoodTuring missing mass estimator show strategy uniformly attains optimal discovery rate macroscopic limit sense assumption probabilistic expert also provide numerical experiment suggesting optimal behavior may still hold weaker assumption Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sanjeev Arora', 'Rong Ge', 'Ravi Kannan', 'Ankur Moitra']

1111.0952v1

In the Nonnegative Matrix Factorization (NMF) problem we are given an $n \times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express $M$ as $A W$ where $A$ and $W$ are nonnegative matrices of size $n \times r$ and $r \times m$ respectively. In some applications, it makes sense to ask instead for the product $AW$ to approximate $M$ -- i.e. (approximately) minimize $\norm{M - AW}_F$ where $\norm{}_F$ denotes the Frobenius norm; we refer to this as Approximate NMF. This problem has a rich history spanning quantum mechanics, probability theory, data analysis, polyhedral combinatorics, communication complexity, demography, chemometrics, etc. In the past decade NMF has become enormously popular in machine learning, where $A$ and $W$ are computed using a variety of local search heuristics. Vavasis proved that this problem is NP-complete. We initiate a study of when this problem is solvable in polynomial time: 1. We give a polynomial-time algorithm for exact and approximate NMF for every constant $r$. Indeed NMF is most interesting in applications precisely when $r$ is small. 2. We complement this with a hardness result, that if exact NMF can be solved in time $(nm)^{o(r)}$, 3-SAT has a sub-exponential time algorithm. This rules out substantial improvements to the above algorithm. 3. We give an algorithm that runs in time polynomial in $n$, $m$ and $r$ under the separablity condition identified by Donoho and Stodden in 2003. The algorithm may be practical since it is simple and noise tolerant (under benign assumptions). Separability is believed to hold in many practical settings. To the best of our knowledge, this last result is the first example of a polynomial-time algorithm that provably works under a non-trivial condition on the input and we believe that this will be an interesting and important direction for future work.

Computing a Nonnegative Matrix Factorization -- Provably

http://arxiv.org/pdf/1111.0952v1

Title Computing Nonnegative Matrix Factorization Provably Summary Nonnegative Matrix Factorization NMF problem given n time nonnegative matrix integer r 0 goal express W W nonnegative matrix size n time r r time respectively application make sense ask instead product AW approximate ie approximately minimize normM AWF normF denotes Frobenius norm refer Approximate NMF problem rich history spanning quantum mechanic probability theory data analysis polyhedral combinatorics communication complexity demography chemometrics etc past decade NMF become enormously popular machine learning W computed using variety local search heuristic Vavasis proved problem NPcomplete initiate study problem solvable polynomial time 1 give polynomialtime algorithm exact approximate NMF every constant r Indeed NMF interesting application precisely r small 2 complement hardness result exact NMF solved time nmor 3SAT subexponential time algorithm rule substantial improvement algorithm 3 give algorithm run time polynomial n r separablity condition identified Donoho Stodden 2003 algorithm may practical since simple noise tolerant benign assumption Separability believed hold many practical setting best knowledge last result first example polynomialtime algorithm provably work nontrivial condition input believe interesting important direction future work Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Lisa Hellerstein', 'Devorah Kletenik', 'Linda Sellie', 'Rocco Servedio']

1111.1124v1

We prove a new structural lemma for partial Boolean functions $f$, which we call the seed lemma for DNF. Using the lemma, we give the first subexponential algorithm for proper learning of DNF in Angluin's Equivalence Query (EQ) model. The algorithm has time and query complexity $2^{(\tilde{O}{\sqrt{n}})}$, which is optimal. We also give a new result on certificates for DNF-size, a simple algorithm for properly PAC-learning DNF, and new results on EQ-learning $\log n$-term DNF and decision trees.

Tight Bounds on Proper Equivalence Query Learning of DNF

http://arxiv.org/pdf/1111.1124v1

Title Tight Bounds Proper Equivalence Query Learning DNF Summary prove new structural lemma partial Boolean function f call seed lemma DNF Using lemma give first subexponential algorithm proper learning DNF Angluins Equivalence Query EQ model algorithm time query complexity 2tildeOsqrtn optimal also give new result certificate DNFsize simple algorithm properly PAClearning DNF new result EQlearning log nterm DNF decision tree Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Yuyang Wang', 'Roni Khardon', 'Pavlos Protopapas']

1111.1315v2

Many real world problems exhibit patterns that have periodic behavior. For example, in astrophysics, periodic variable stars play a pivotal role in understanding our universe. An important step when analyzing data from such processes is the problem of identifying the period: estimating the period of a periodic function based on noisy observations made at irregularly spaced time points. This problem is still a difficult challenge despite extensive study in different disciplines. The paper makes several contributions toward solving this problem. First, we present a nonparametric Bayesian model for period finding, based on Gaussian Processes (GP), that does not make strong assumptions on the shape of the periodic function. As our experiments demonstrate, the new model leads to significantly better results in period estimation when the target function is non-sinusoidal. Second, we develop a new algorithm for parameter optimization for GP which is useful when the likelihood function is very sensitive to the setting of the hyper-parameters with numerous local minima, as in the case of period estimation. The algorithm combines gradient optimization with grid search and incorporates several mechanisms to overcome the high complexity of inference with GP. Third, we develop a novel approach for using domain knowledge, in the form of a probabilistic generative model, and incorporate it into the period estimation algorithm. Experimental results on astrophysics data validate our approach showing significant improvement over the state of the art in this domain.

Nonparametric Bayesian Estimation of Periodic Functions

http://arxiv.org/pdf/1111.1315v2

Title Nonparametric Bayesian Estimation Periodic Functions Summary Many real world problem exhibit pattern periodic behavior example astrophysics periodic variable star play pivotal role understanding universe important step analyzing data process problem identifying period estimating period periodic function based noisy observation made irregularly spaced time point problem still difficult challenge despite extensive study different discipline paper make several contribution toward solving problem First present nonparametric Bayesian model period finding based Gaussian Processes GP make strong assumption shape periodic function experiment demonstrate new model lead significantly better result period estimation target function nonsinusoidal Second develop new algorithm parameter optimization GP useful likelihood function sensitive setting hyperparameters numerous local minimum case period estimation algorithm combine gradient optimization grid search incorporates several mechanism overcome high complexity inference GP Third develop novel approach using domain knowledge form probabilistic generative model incorporate period estimation algorithm Experimental result astrophysics data validate approach showing significant improvement state art domain Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shipra Agrawal', 'Navin Goyal']

1111.1797v3

The multi-armed bandit problem is a popular model for studying exploration/exploitation trade-off in sequential decision problems. Many algorithms are now available for this well-studied problem. One of the earliest algorithms, given by W. R. Thompson, dates back to 1933. This algorithm, referred to as Thompson Sampling, is a natural Bayesian algorithm. The basic idea is to choose an arm to play according to its probability of being the best arm. Thompson Sampling algorithm has experimentally been shown to be close to optimal. In addition, it is efficient to implement and exhibits several desirable properties such as small regret for delayed feedback. However, theoretical understanding of this algorithm was quite limited. In this paper, for the first time, we show that Thompson Sampling algorithm achieves logarithmic expected regret for the multi-armed bandit problem. More precisely, for the two-armed bandit problem, the expected regret in time $T$ is $O(\frac{\ln T}{\Delta} + \frac{1}{\Delta^3})$. And, for the $N$-armed bandit problem, the expected regret in time $T$ is $O([(\sum_{i=2}^N \frac{1}{\Delta_i^2})^2] \ln T)$. Our bounds are optimal but for the dependence on $\Delta_i$ and the constant factors in big-Oh.

Analysis of Thompson Sampling for the multi-armed bandit problem

http://arxiv.org/pdf/1111.1797v3

Title Analysis Thompson Sampling multiarmed bandit problem Summary multiarmed bandit problem popular model studying explorationexploitation tradeoff sequential decision problem Many algorithm available wellstudied problem One earliest algorithm given W R Thompson date back 1933 algorithm referred Thompson Sampling natural Bayesian algorithm basic idea choose arm play according probability best arm Thompson Sampling algorithm experimentally shown close optimal addition efficient implement exhibit several desirable property small regret delayed feedback However theoretical understanding algorithm quite limited paper first time show Thompson Sampling algorithm achieves logarithmic expected regret multiarmed bandit problem precisely twoarmed bandit problem expected regret time Ofracln TDelta frac1Delta3 Narmed bandit problem expected regret time Osumi2N frac1Deltai22 ln bound optimal dependence Deltai constant factor bigOh Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sanmay Das', 'Allen Lavoie', 'Malik Magdon-Ismail']

1111.2092v1

As a major source for information on virtually any topic, Wikipedia serves an important role in public dissemination and consumption of knowledge. As a result, it presents tremendous potential for people to promulgate their own points of view; such efforts may be more subtle than typical vandalism. In this paper, we introduce new behavioral metrics to quantify the level of controversy associated with a particular user: a Controversy Score (C-Score) based on the amount of attention the user focuses on controversial pages, and a Clustered Controversy Score (CC-Score) that also takes into account topical clustering. We show that both these measures are useful for identifying people who try to "push" their points of view, by showing that they are good predictors of which editors get blocked. The metrics can be used to triage potential POV pushers. We apply this idea to a dataset of users who requested promotion to administrator status and easily identify some editors who significantly changed their behavior upon becoming administrators. At the same time, such behavior is not rampant. Those who are promoted to administrator status tend to have more stable behavior than comparable groups of prolific editors. This suggests that the Adminship process works well, and that the Wikipedia community is not overwhelmed by users who become administrators to promote their own points of view.

Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia

http://arxiv.org/pdf/1111.2092v1

Title Pushing Point View Behavioral Measures Manipulation Wikipedia Summary major source information virtually topic Wikipedia serf important role public dissemination consumption knowledge result present tremendous potential people promulgate point view effort may subtle typical vandalism paper introduce new behavioral metric quantify level controversy associated particular user Controversy Score CScore based amount attention user focus controversial page Clustered Controversy Score CCScore also take account topical clustering show measure useful identifying people try push point view showing good predictor editor get blocked metric used triage potential POV pusher apply idea dataset user requested promotion administrator status easily identify editor significantly changed behavior upon becoming administrator time behavior rampant promoted administrator status tend stable behavior comparable group prolific editor suggests Adminship process work well Wikipedia community overwhelmed user become administrator promote point view Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Song Liu', 'Peter Flach', 'Nello Cristianini']

1111.2111v2

In this paper we introduce a generic model for multiplicative algorithms which is suitable for the MapReduce parallel programming paradigm. We implement three typical machine learning algorithms to demonstrate how similarity comparison, gradient descent, power method and other classic learning techniques fit this model well. Two versions of large-scale matrix multiplication are discussed in this paper, and different methods are developed for both cases with regard to their unique computational characteristics and problem settings. In contrast to earlier research, we focus on fundamental linear algebra techniques that establish a generic approach for a range of algorithms, rather than specific ways of scaling up algorithms one at a time. Experiments show promising results when evaluated on both speedup and accuracy. Compared with a standard implementation with computational complexity $O(m^3)$ in the worst case, the large-scale matrix multiplication experiments prove our design is considerably more efficient and maintains a good speedup as the number of cores increases. Algorithm-specific experiments also produce encouraging results on runtime performance.

Generic Multiplicative Methods for Implementing Machine Learning Algorithms on MapReduce

http://arxiv.org/pdf/1111.2111v2

Title Generic Multiplicative Methods Implementing Machine Learning Algorithms MapReduce Summary paper introduce generic model multiplicative algorithm suitable MapReduce parallel programming paradigm implement three typical machine learning algorithm demonstrate similarity comparison gradient descent power method classic learning technique fit model well Two version largescale matrix multiplication discussed paper different method developed case regard unique computational characteristic problem setting contrast earlier research focus fundamental linear algebra technique establish generic approach range algorithm rather specific way scaling algorithm one time Experiments show promising result evaluated speedup accuracy Compared standard implementation computational complexity Om3 worst case largescale matrix multiplication experiment prove design considerably efficient maintains good speedup number core increase Algorithmspecific experiment also produce encouraging result runtime performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rong Jin', 'Tianbao Yang', 'Mehrdad Mahdavi', 'Yu-Feng Li', 'Zhi-Hua Zhou']

1111.2262v4

We develop two approaches for analyzing the approximation error bound for the Nystr\"{o}m method, one based on the concentration inequality of integral operator, and one based on the compressive sensing theory. We show that the approximation error, measured in the spectral norm, can be improved from $O(N/\sqrt{m})$ to $O(N/m^{1 - \rho})$ in the case of large eigengap, where $N$ is the total number of data points, $m$ is the number of sampled data points, and $\rho \in (0, 1/2)$ is a positive constant that characterizes the eigengap. When the eigenvalues of the kernel matrix follow a $p$-power law, our analysis based on compressive sensing theory further improves the bound to $O(N/m^{p - 1})$ under an incoherence assumption, which explains why the Nystr\"{o}m method works well for kernel matrix with skewed eigenvalues. We present a kernel classification approach based on the Nystr\"{o}m method and derive its generalization performance using the improved bound. We show that when the eigenvalues of kernel matrix follow a $p$-power law, we can reduce the number of support vectors to $N^{2p/(p^2 - 1)}$, a number less than $N$ when $p > 1+\sqrt{2}$, without seriously sacrificing its generalization performance.

Improved Bound for the Nystrom's Method and its Application to Kernel Classification

http://arxiv.org/pdf/1111.2262v4

Title Improved Bound Nystroms Method Application Kernel Classification Summary develop two approach analyzing approximation error bound Nystrom method one based concentration inequality integral operator one based compressive sensing theory show approximation error measured spectral norm improved ONsqrtm ONm1 rho case large eigengap N total number data point number sampled data point rho 0 12 positive constant characterizes eigengap eigenvalue kernel matrix follow ppower law analysis based compressive sensing theory improves bound ONmp 1 incoherence assumption explains Nystrom method work well kernel matrix skewed eigenvalue present kernel classification approach based Nystrom method derive generalization performance using improved bound show eigenvalue kernel matrix follow ppower law reduce number support vector N2pp2 1 number le N p 1sqrt2 without seriously sacrificing generalization performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jacob Abernethy', 'Rafael M. Frongillo']

1111.2664v1

Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of "crowdsourcing" prediction tasks. But these competitions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively "learn" a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and participants can modify this hypothesis by wagering on an update. The critical incentive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set.

A Collaborative Mechanism for Crowdsourcing Prediction Problems

http://arxiv.org/pdf/1111.2664v1

Title Collaborative Mechanism Crowdsourcing Prediction Problems Summary Machine Learning competition Netflix Prize proven reasonably successful method crowdsourcing prediction task competition number weakness particularly incentive structure create participant propose new approach called Crowdsourced Learning Mechanism participant collaboratively learn hypothesis given prediction task approach draw heavily concept prediction market trader bet likelihood future event framework mechanism continues publish current hypothesis participant modify hypothesis wagering update critical incentive property participant profit amount scale according much update improves performance released test set Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Marcos A. Domingues', 'Alipio Mario Jorge', 'Carlos Soares']

1111.2948v2

Traditionally, recommender systems for the Web deal with applications that have two dimensions, users and items. Based on access logs that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a method to complement the information in the access logs with contextual information without changing the recommendation algorithm. The method consists in representing context as virtual items. We empirically test this method with two top-N recommender systems, an item-based collaborative filtering technique and association rules, on three data sets. The results show that our method is able to take advantage of the context (new dimensions) when it is informative.

Using Contextual Information as Virtual Items on Top-N Recommender Systems

http://arxiv.org/pdf/1111.2948v2

Title Using Contextual Information Virtual Items TopN Recommender Systems Summary Traditionally recommender system Web deal application two dimension user item Based access log relate dimension recommendation model built used identify set N item interest certain user paper propose method complement information access log contextual information without changing recommendation algorithm method consists representing context virtual item empirically test method two topN recommender system itembased collaborative filtering technique association rule three data set result show method able take advantage context new dimension informative Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Matthew D. Hoffman', 'Andrew Gelman']

1111.4246v1

Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information. These features allow it to converge to high-dimensional target distributions much more quickly than simpler methods such as random walk Metropolis or Gibbs sampling. However, HMC's performance is highly sensitive to two user-specified parameters: a step size {\epsilon} and a desired number of steps L. In particular, if L is too small then the algorithm exhibits undesirable random walk behavior, while if L is too large the algorithm wastes computation. We introduce the No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L. NUTS uses a recursive algorithm to build a set of likely candidate points that spans a wide swath of the target distribution, stopping automatically when it starts to double back and retrace its steps. Empirically, NUTS perform at least as efficiently as and sometimes more efficiently than a well tuned standard HMC method, without requiring user intervention or costly tuning runs. We also derive a method for adapting the step size parameter {\epsilon} on the fly based on primal-dual averaging. NUTS can thus be used with no hand-tuning at all. NUTS is also suitable for applications such as BUGS-style automatic inference engines that require efficient "turnkey" sampling algorithms.

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

http://arxiv.org/pdf/1111.4246v1

Title NoUTurn Sampler Adaptively Setting Path Lengths Hamiltonian Monte Carlo Summary Hamiltonian Monte Carlo HMC Markov chain Monte Carlo MCMC algorithm avoids random walk behavior sensitivity correlated parameter plague many MCMC method taking series step informed firstorder gradient information feature allow converge highdimensional target distribution much quickly simpler method random walk Metropolis Gibbs sampling However HMCs performance highly sensitive two userspecified parameter step size epsilon desired number step L particular L small algorithm exhibit undesirable random walk behavior L large algorithm waste computation introduce NoUTurn Sampler NUTS extension HMC eliminates need set number step L NUTS us recursive algorithm build set likely candidate point span wide swath target distribution stopping automatically start double back retrace step Empirically NUTS perform least efficiently sometimes efficiently well tuned standard HMC method without requiring user intervention costly tuning run also derive method adapting step size parameter epsilon fly based primaldual averaging NUTS thus used handtuning NUTS also suitable application BUGSstyle automatic inference engine require efficient turnkey sampling algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Francis Bach']

1111.6453v2

Submodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions and (2) the lovasz extension of submodular functions provides a useful set of regularization functions for supervised and unsupervised learning. In this monograph, we present the theory of submodular functions from a convex analysis perspective, presenting tight links between certain polyhedra, combinatorial optimization and convex optimization problems. In particular, we show how submodular function minimization is equivalent to solving a wide variety of convex optimization problems. This allows the derivation of new efficient algorithms for approximate and exact submodular function minimization with theoretical guarantees and good practical performance. By listing many examples of submodular functions, we review various applications to machine learning, such as clustering, experimental design, sensor placement, graphical model structure learning or subset selection, as well as a family of structured sparsity-inducing norms that can be derived and used from submodular functions.

Learning with Submodular Functions: A Convex Optimization Perspective

http://arxiv.org/pdf/1111.6453v2

Title Learning Submodular Functions Convex Optimization Perspective Summary Submodular function relevant machine learning least two reason 1 problem may expressed directly optimization submodular function 2 lovasz extension submodular function provides useful set regularization function supervised unsupervised learning monograph present theory submodular function convex analysis perspective presenting tight link certain polyhedron combinatorial optimization convex optimization problem particular show submodular function minimization equivalent solving wide variety convex optimization problem allows derivation new efficient algorithm approximate exact submodular function minimization theoretical guarantee good practical performance listing many example submodular function review various application machine learning clustering experimental design sensor placement graphical model structure learning subset selection well family structured sparsityinducing norm derived used submodular function Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Raajay Viswanathan', 'Prateek Jain', 'Srivatsan Laxman', 'Arvind Arasu']

1111.7295v2

In this paper, we consider the problem of estimating self-tuning histograms using query workloads. To this end, we propose a general learning theoretic formulation. Specifically, we use query feedback from a workload as training data to estimate a histogram with a small memory footprint that minimizes the expected error on future queries. Our formulation provides a framework in which different approaches can be studied and developed. We first study the simple class of equi-width histograms and present a learning algorithm, EquiHist, that is competitive in many settings. We also provide formal guarantees for equi-width histograms that highlight scenarios in which equi-width histograms can be expected to succeed or fail. We then go beyond equi-width histograms and present a novel learning algorithm, SpHist, for estimating general histograms. Here we use Haar wavelets to reduce the problem of learning histograms to that of learning a sparse vector. Both algorithms have multiple advantages over existing methods: 1) simple and scalable extensions to multi-dimensional data, 2) scalability with number of histogram buckets and size of query feedback, 3) natural extensions to incorporate new feedback and handle database updates. We demonstrate these advantages over the current state-of-the-art, ISOMER, through detailed experiments on real and synthetic data. In particular, we show that SpHist obtains up to 50% less error than ISOMER on real-world multi-dimensional datasets.

A Learning Framework for Self-Tuning Histograms

http://arxiv.org/pdf/1111.7295v2

Title Learning Framework SelfTuning Histograms Summary paper consider problem estimating selftuning histogram using query workload end propose general learning theoretic formulation Specifically use query feedback workload training data estimate histogram small memory footprint minimizes expected error future query formulation provides framework different approach studied developed first study simple class equiwidth histogram present learning algorithm EquiHist competitive many setting also provide formal guarantee equiwidth histogram highlight scenario equiwidth histogram expected succeed fail go beyond equiwidth histogram present novel learning algorithm SpHist estimating general histogram use Haar wavelet reduce problem learning histogram learning sparse vector algorithm multiple advantage existing method 1 simple scalable extension multidimensional data 2 scalability number histogram bucket size query feedback 3 natural extension incorporate new feedback handle database update demonstrate advantage current stateoftheart ISOMER detailed experiment real synthetic data particular show SpHist obtains 50 le error ISOMER realworld multidimensional datasets Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Fabian Pedregosa', 'Gaël Varoquaux', 'Alexandre Gramfort', 'Vincent Michel', 'Bertrand Thirion', 'Olivier Grisel', 'Mathieu Blondel', 'Gilles Louppe', 'Peter Prettenhofer', 'Ron Weiss', 'Vincent Dubourg', 'Jake Vanderplas', 'Alexandre Passos', 'David Cournapeau', 'Matthieu Brucher', 'Matthieu Perrot', 'Édouard Duchesnay']

1201.0490v3

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

Scikit-learn: Machine Learning in Python

http://arxiv.org/pdf/1201.0490v3

Title Scikitlearn Machine Learning Python Summary Scikitlearn Python module integrating wide range stateoftheart machine learning algorithm mediumscale supervised unsupervised problem package focus bringing machine learning nonspecialists using generalpurpose highlevel language Emphasis put ease use performance documentation API consistency minimal dependency distributed simplified BSD license encouraging use academic commercial setting Source code binary documentation downloaded httpscikitlearnsourceforgenet Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Sung-eok Jeon', 'Chuanyi Ji']

1201.2575v2

For a large multi-hop wireless network, nodes are preferable to make distributed and localized link-scheduling decisions with only interactions among a small number of neighbors. However, for a slowly decaying channel and densely populated interferers, a small size neighborhood often results in nontrivial link outages and is thus insufficient for making optimal scheduling decisions. A question arises how to deal with the information outside a neighborhood in distributed link-scheduling. In this work, we develop joint approximation of information and distributed link scheduling. We first apply machine learning approaches to model distributed link-scheduling with complete information. We then characterize the information outside a neighborhood in form of residual interference as a random loss variable. The loss variable is further characterized by either a Mean Field approximation or a normal distribution based on the Lyapunov central limit theorem. The approximated information outside a neighborhood is incorporated in a factor graph. This results in joint approximation and distributed link-scheduling in an iterative fashion. Link-scheduling decisions are first made at each individual node based on the approximated loss variables. Loss variables are then updated and used for next link-scheduling decisions. The algorithm repeats between these two phases until convergence. Interactive iterations among these variables are implemented with a message-passing algorithm over a factor graph. Simulation results show that using learned information outside a neighborhood jointly with distributed link-scheduling reduces the outage probability close to zero even for a small neighborhood.

Joint Approximation of Information and Distributed Link-Scheduling Decisions in Wireless Networks

http://arxiv.org/pdf/1201.2575v2

Title Joint Approximation Information Distributed LinkScheduling Decisions Wireless Networks Summary large multihop wireless network node preferable make distributed localized linkscheduling decision interaction among small number neighbor However slowly decaying channel densely populated interferers small size neighborhood often result nontrivial link outage thus insufficient making optimal scheduling decision question arises deal information outside neighborhood distributed linkscheduling work develop joint approximation information distributed link scheduling first apply machine learning approach model distributed linkscheduling complete information characterize information outside neighborhood form residual interference random loss variable loss variable characterized either Mean Field approximation normal distribution based Lyapunov central limit theorem approximated information outside neighborhood incorporated factor graph result joint approximation distributed linkscheduling iterative fashion Linkscheduling decision first made individual node based approximated loss variable Loss variable updated used next linkscheduling decision algorithm repeat two phase convergence Interactive iteration among variable implemented messagepassing algorithm factor graph Simulation result show using learned information outside neighborhood jointly distributed linkscheduling reduces outage probability close zero even small neighborhood Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Geetha Manjunatha', 'M Narasimha Murty', 'Dinkar Sitaram']

1201.2925v2

Most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a 'flat' form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets, namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy.

Combining Heterogeneous Classifiers for Relational Databases

http://arxiv.org/pdf/1201.2925v2

Title Combining Heterogeneous Classifiers Relational Databases Summary enterprise data distributed multiple relational database expertdesigned schema Using traditional singletable machine learning technique data incur computational penalty converting flat form megajoin even humanspecified semantic information present relation lost paper present practical twophase hierarchical metaclassification algorithm relational database semantic divide conquer approach propose recursive prediction aggregation technique heterogeneous classifier applied individual database table proposed algorithm evaluated three diverse datasets namely TPCH PKDD UCI benchmark showed considerable reduction classification time without loss prediction accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Keqin Liu', 'Qing Zhao']

1201.4906v1

We consider the adaptive shortest-path routing problem in wireless networks under unknown and stochastically varying link states. In this problem, we aim to optimize the quality of communication between a source and a destination through adaptive path selection. Due to the randomness and uncertainties in the network dynamics, the quality of each link varies over time according to a stochastic process with unknown distributions. After a path is selected for communication, the aggregated quality of all links on this path (e.g., total path delay) is observed. The quality of each individual link is not observable. We formulate this problem as a multi-armed bandit with dependent arms. We show that by exploiting arm dependencies, a regret polynomial with network size can be achieved while maintaining the optimal logarithmic order with time. This is in sharp contrast with the exponential regret order with network size offered by a direct application of the classic MAB policies that ignore arm dependencies. Furthermore, our results are obtained under a general model of link-quality distributions (including heavy-tailed distributions) and find applications in cognitive radio and ad hoc networks with unknown and dynamic communication environments.

Adaptive Shortest-Path Routing under Unknown and Stochastically Varying Link States

http://arxiv.org/pdf/1201.4906v1

Title Adaptive ShortestPath Routing Unknown Stochastically Varying Link States Summary consider adaptive shortestpath routing problem wireless network unknown stochastically varying link state problem aim optimize quality communication source destination adaptive path selection Due randomness uncertainty network dynamic quality link varies time according stochastic process unknown distribution path selected communication aggregated quality link path eg total path delay observed quality individual link observable formulate problem multiarmed bandit dependent arm show exploiting arm dependency regret polynomial network size achieved maintaining optimal logarithmic order time sharp contrast exponential regret order network size offered direct application classic MAB policy ignore arm dependency Furthermore result obtained general model linkquality distribution including heavytailed distribution find application cognitive radio ad hoc network unknown dynamic communication environment Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Bo Zhao', 'Benjamin I. P. Rubinstein', 'Jim Gemmell', 'Jiawei Han']

1203.0058v1

In practical data integration systems, it is common for the data sources being integrated to provide conflicting information about the same entity. Consequently, a major challenge for data integration is to derive the most complete and accurate integrated records from diverse and sometimes conflicting sources. We term this challenge the truth finding problem. We observe that some sources are generally more reliable than others, and therefore a good model of source quality is the key to solving the truth finding problem. In this work, we propose a probabilistic graphical model that can automatically infer true records and source quality without any supervision. In contrast to previous methods, our principled approach leverages a generative process of two types of errors (false positive and false negative) by modeling two different aspects of source quality. In so doing, ours is also the first approach designed to merge multi-valued attribute types. Our method is scalable, due to an efficient sampling-based inference algorithm that needs very few iterations in practice and enjoys linear time complexity, with an even faster incremental variant. Experiments on two real world datasets show that our new method outperforms existing state-of-the-art approaches to the truth finding problem.

A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration

http://arxiv.org/pdf/1203.0058v1

Title Bayesian Approach Discovering Truth Conflicting Sources Data Integration Summary practical data integration system common data source integrated provide conflicting information entity Consequently major challenge data integration derive complete accurate integrated record diverse sometimes conflicting source term challenge truth finding problem observe source generally reliable others therefore good model source quality key solving truth finding problem work propose probabilistic graphical model automatically infer true record source quality without supervision contrast previous method principled approach leverage generative process two type error false positive false negative modeling two different aspect source quality also first approach designed merge multivalued attribute type method scalable due efficient samplingbased inference algorithm need iteration practice enjoys linear time complexity even faster incremental variant Experiments two real world datasets show new method outperforms existing stateoftheart approach truth finding problem Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['T Soni Madhulatha']

1203.2002v1

Clustering is a common technique for statistical data analysis, Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. Often, distance measures are used. Clustering is an unsupervised learning technique, where interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. This paper also considers the partitioning of m-dimensional lattice graphs using Fiedler's approach, which requires the determination of the eigenvector belonging to the second smallest Eigenvalue of the Laplacian with K-means partitioning algorithm.

Graph partitioning advance clustering technique

http://arxiv.org/pdf/1203.2002v1

Title Graph partitioning advance clustering technique Summary Clustering common technique statistical data analysis Clustering process grouping data class cluster object within cluster high similarity comparison one another dissimilar object cluster Dissimilarities assessed based attribute value describing object Often distance measure used Clustering unsupervised learning technique interesting pattern structure found directly large data set little none background knowledge paper also considers partitioning mdimensional lattice graph using Fiedlers approach requires determination eigenvector belonging second smallest Eigenvalue Laplacian Kmeans partitioning algorithm Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Surjeet Kumar Yadav', 'Brijesh Bharadwaj', 'Saurabh Pal']

1203.2987v1

The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a course. This paper presents a data mining project to generate predictive models for student retention management. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. This paper examines the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machines learning algorithms are able to establish effective predictive models from the existing student retention data.

Mining Education Data to Predict Student's Retention: A comparative Study

http://arxiv.org/pdf/1203.2987v1

Title Mining Education Data Predict Students Retention comparative Study Summary main objective higher education provide quality education student One way achieve highest level quality higher education system discovering knowledge prediction regarding enrolment student course paper present data mining project generate predictive model student retention management Given new record incoming student predictive model produce short accurate prediction list identifying student tend need support student retention program paper examines quality predictive model generated machine learning algorithm result show machine learning algorithm able establish effective predictive model existing student retention data Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Hussein Saad', 'Amr Mohamed', 'Tamer ElBatt']

1203.3935v1

In this paper, we propose a distributed reinforcement learning (RL) technique called distributed power control using Q-learning (DPC-Q) to manage the interference caused by the femtocells on macro-users in the downlink. The DPC-Q leverages Q-Learning to identify the sub-optimal pattern of power allocation, which strives to maximize femtocell capacity, while guaranteeing macrocell capacity level in an underlay cognitive setting. We propose two different approaches for the DPC-Q algorithm: namely, independent, and cooperative. In the former, femtocells learn independently from each other while in the latter, femtocells share some information during learning in order to enhance their performance. Simulation results show that the independent approach is capable of mitigating the interference generated by the femtocells on macro-users. Moreover, the results show that cooperation enhances the performance of the femtocells in terms of speed of convergence, fairness and aggregate femtocell capacity.

Distributed Cooperative Q-learning for Power Allocation in Cognitive Femtocell Networks

http://arxiv.org/pdf/1203.3935v1

Title Distributed Cooperative Qlearning Power Allocation Cognitive Femtocell Networks Summary paper propose distributed reinforcement learning RL technique called distributed power control using Qlearning DPCQ manage interference caused femtocells macrousers downlink DPCQ leverage QLearning identify suboptimal pattern power allocation strives maximize femtocell capacity guaranteeing macrocell capacity level underlay cognitive setting propose two different approach DPCQ algorithm namely independent cooperative former femtocells learn independently latter femtocells share information learning order enhance performance Simulation result show independent approach capable mitigating interference generated femtocells macrousers Moreover result show cooperation enhances performance femtocells term speed convergence fairness aggregate femtocell capacity Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Rajiv Khanna', 'Liang Zhang', 'Deepak Agarwal', 'Beechung Chen']

1203.5124v1

Predicting user affinity to items is an important problem in applications like content optimization, computational advertising, and many more. While bilinear random effect models (matrix factorization) provide state-of-the-art performance when minimizing RMSE through a Gaussian response model on explicit ratings data, applying it to imbalanced binary response data presents additional challenges that we carefully study in this paper. Data in many applications usually consist of users' implicit response that are often binary -- clicking an item or not; the goal is to predict click rates, which is often combined with other measures to calculate utilities to rank items at runtime of the recommender systems. Because of the implicit nature, such data are usually much larger than explicit rating data and often have an imbalanced distribution with a small fraction of click events, making accurate click rate prediction difficult. In this paper, we address two problems. First, we show previous techniques to estimate bilinear random effect models with binary data are less accurate compared to our new approach based on adaptive rejection sampling, especially for imbalanced response. Second, we develop a parallel bilinear random effect model fitting framework using Map-Reduce paradigm that scales to massive datasets. Our parallel algorithm is based on a "divide and conquer" strategy coupled with an ensemble approach. Through experiments on the benchmark MovieLens data, a small Yahoo! Front Page data set, and a large Yahoo! Front Page data set that contains 8M users and 1B binary observations, we show that careful handling of binary response as well as identifiability issues are needed to achieve good performance for click rate prediction, and that the proposed adaptive rejection sampler and the partitioning as well as ensemble techniques significantly improve model performance.

Parallel Matrix Factorization for Binary Response

http://arxiv.org/pdf/1203.5124v1

Title Parallel Matrix Factorization Binary Response Summary Predicting user affinity item important problem application like content optimization computational advertising many bilinear random effect model matrix factorization provide stateoftheart performance minimizing RMSE Gaussian response model explicit rating data applying imbalanced binary response data present additional challenge carefully study paper Data many application usually consist user implicit response often binary clicking item goal predict click rate often combined measure calculate utility rank item runtime recommender system implicit nature data usually much larger explicit rating data often imbalanced distribution small fraction click event making accurate click rate prediction difficult paper address two problem First show previous technique estimate bilinear random effect model binary data le accurate compared new approach based adaptive rejection sampling especially imbalanced response Second develop parallel bilinear random effect model fitting framework using MapReduce paradigm scale massive datasets parallel algorithm based divide conquer strategy coupled ensemble approach experiment benchmark MovieLens data small Yahoo Front Page data set large Yahoo Front Page data set contains 8M user 1B binary observation show careful handling binary response well identifiability issue needed achieve good performance click rate prediction proposed adaptive rejection sampler partitioning well ensemble technique significantly improve model performance Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Philippe Lauret', 'Auline Rodler', 'Marc Muselli', 'Mathieu David', 'Hadja Diagne', 'Cyril Voyant']

1203.5446v1

This paper proposes to use a rather new modelling approach in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving Average (ARMA) and Neural Network (NN) models are combined to form a model committee. The Bayesian inference is used to affect a probability to each model in the committee. Hence, each model's predictions are weighted by their respective probability. The models are fitted to one year of hourly Global Horizontal Irradiance (GHI) measurements. Another year (the test set) is used for making genuine one hour ahead (h+1) out-of-sample forecast comparisons. The proposed approach is benchmarked against the persistence model. The very first results show an improvement brought by this approach.

A Bayesian Model Committee Approach to Forecasting Global Solar Radiation

http://arxiv.org/pdf/1203.5446v1

Title Bayesian Model Committee Approach Forecasting Global Solar Radiation Summary paper proposes use rather new modelling approach realm solar radiation forecasting work two forecasting model Autoregressive Moving Average ARMA Neural Network NN model combined form model committee Bayesian inference used affect probability model committee Hence model prediction weighted respective probability model fitted one year hourly Global Horizontal Irradiance GHI measurement Another year test set used making genuine one hour ahead h1 outofsample forecast comparison proposed approach benchmarked persistence model first result show improvement brought approach Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Elad Hazan', 'Satyen Kale', 'Shai Shalev-Shwartz']

1204.0136v1

In several online prediction problems of recent interest the comparison class is composed of matrices with bounded entries. For example, in the online max-cut problem, the comparison class is matrices which represent cuts of a given graph and in online gambling the comparison class is matrices which represent permutations over n teams. Another important example is online collaborative filtering in which a widely used comparison class is the set of matrices with a small trace norm. In this paper we isolate a property of matrices, which we call (beta,tau)-decomposability, and derive an efficient online learning algorithm, that enjoys a regret bound of O*(sqrt(beta tau T)) for all problems in which the comparison class is composed of (beta,tau)-decomposable matrices. By analyzing the decomposability of cut matrices, triangular matrices, and low trace-norm matrices, we derive near optimal regret bounds for online max-cut, online gambling, and online collaborative filtering. In particular, this resolves (in the affirmative) an open problem posed by Abernethy (2010); Kleinberg et al (2010). Finally, we derive lower bounds for the three problems and show that our upper bounds are optimal up to logarithmic factors. In particular, our lower bound for the online collaborative filtering problem resolves another open problem posed by Shamir and Srebro (2011).

Near-Optimal Algorithms for Online Matrix Prediction

http://arxiv.org/pdf/1204.0136v1

Title NearOptimal Algorithms Online Matrix Prediction Summary several online prediction problem recent interest comparison class composed matrix bounded entry example online maxcut problem comparison class matrix represent cut given graph online gambling comparison class matrix represent permutation n team Another important example online collaborative filtering widely used comparison class set matrix small trace norm paper isolate property matrix call betataudecomposability derive efficient online learning algorithm enjoys regret bound Osqrtbeta tau problem comparison class composed betataudecomposable matrix analyzing decomposability cut matrix triangular matrix low tracenorm matrix derive near optimal regret bound online maxcut online gambling online collaborative filtering particular resolve affirmative open problem posed Abernethy 2010 Kleinberg et al 2010 Finally derive lower bound three problem show upper bound optimal logarithmic factor particular lower bound online collaborative filtering problem resolve another open problem posed Shamir Srebro 2011 Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Jia Zeng', 'Zhi-Qiang Liu', 'Xiao-Qin Cao']

1204.0170v2

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic space. To process massive corpora having a large number of topics, the training iteration of batch LDA algorithms is often inefficient and time-consuming. To accelerate the training speed, ABP actively scans the subset of corpus and searches the subset of topic space for topic modeling, therefore saves enormous training time in each iteration. To ensure accuracy, ABP selects only those documents and topics that contribute to the largest residuals within the residual belief propagation (RBP) framework. On four real-world corpora, ABP performs around $10$ to $100$ times faster than state-of-the-art batch LDA algorithms with a comparable topic modeling accuracy.

A New Approach to Speeding Up Topic Modeling

http://arxiv.org/pdf/1204.0170v2

Title New Approach Speeding Topic Modeling Summary Latent Dirichlet allocation LDA widelyused probabilistic topic modeling paradigm recently find many application computer vision computational biology paper propose fast accurate batch algorithm active belief propagation ABP training LDA Usually batch LDA algorithm require repeated scanning entire corpus searching complete topic space process massive corpus large number topic training iteration batch LDA algorithm often inefficient timeconsuming accelerate training speed ABP actively scan subset corpus search subset topic space topic modeling therefore save enormous training time iteration ensure accuracy ABP selects document topic contribute largest residual within residual belief propagation RBP framework four realworld corpus ABP performs around 10 100 time faster stateoftheart batch LDA algorithm comparable topic modeling accuracy Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Ali Soltan Mohammadi', 'L. Asadzadeh', 'D. D. Rezaee']

1204.1467v1

The rough-set theory proposed by Pawlak, has been widely used in dealing with data classification problems. The original rough-set model is, however, quite sensitive to noisy data. Tzung thus proposed deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from quantitative data with a predefined tolerance degree of uncertainty and misclassification. This model allowed, which combines the variable precision rough-set model and the fuzzy set theory, is thus proposed to solve this problem. This paper thus deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from incomplete quantitative data with a predefined tolerance degree of uncertainty and misclassification. A new method, incomplete quantitative data for rough-set model and the fuzzy set theory, is thus proposed to solve this problem. It first transforms each quantitative value into a fuzzy set of linguistic terms using membership functions and then finding incomplete quantitative data with lower and the fuzzy upper approximations. It second calculates the fuzzy {\beta}-lower and the fuzzy {\beta}-upper approximations. The certain and possible rules are then generated based on these fuzzy approximations. These rules can then be used to classify unknown objects.

Learning Fuzzy β-Certain and β-Possible rules from incomplete quantitative data by rough sets

http://arxiv.org/pdf/1204.1467v1

Title Learning Fuzzy βCertain βPossible rule incomplete quantitative data rough set Summary roughset theory proposed Pawlak widely used dealing data classification problem original roughset model however quite sensitive noisy data Tzung thus proposed deal problem producing set fuzzy certain fuzzy possible rule quantitative data predefined tolerance degree uncertainty misclassification model allowed combine variable precision roughset model fuzzy set theory thus proposed solve problem paper thus deal problem producing set fuzzy certain fuzzy possible rule incomplete quantitative data predefined tolerance degree uncertainty misclassification new method incomplete quantitative data roughset model fuzzy set theory thus proposed solve problem first transforms quantitative value fuzzy set linguistic term using membership function finding incomplete quantitative data lower fuzzy upper approximation second calculates fuzzy betalower fuzzy betaupper approximation certain possible rule generated based fuzzy approximation rule used classify unknown object Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Paulo F. C. Tilles', 'Jose F. Fontanari']

1204.1564v4

An explanation for the acquisition of word-object mappings is the associative learning in a cross-situational scenario. Here we present analytical results of the performance of a simple associative learning algorithm for acquiring a one-to-one mapping between $N$ objects and $N$ words based solely on the co-occurrence between objects and words. In particular, a learning trial in our learning scenario consists of the presentation of $C + 1 < N$ objects together with a target word, which refers to one of the objects in the context. We find that the learning times are distributed exponentially and the learning rates are given by $\ln{[\frac{N(N-1)}{C + (N-1)^{2}}]}$ in the case the $N$ target words are sampled randomly and by $\frac{1}{N} \ln [\frac{N-1}{C}] $ in the case they follow a deterministic presentation sequence. This learning performance is much superior to those exhibited by humans and more realistic learning algorithms in cross-situational experiments. We show that introduction of discrimination limitations using Weber's law and forgetting reduce the performance of the associative algorithm to the human level.

Minimal model of associative learning for cross-situational lexicon acquisition

http://arxiv.org/pdf/1204.1564v4

Title Minimal model associative learning crosssituational lexicon acquisition Summary explanation acquisition wordobject mapping associative learning crosssituational scenario present analytical result performance simple associative learning algorithm acquiring onetoone mapping N object N word based solely cooccurrence object word particular learning trial learning scenario consists presentation C 1 N object together target word refers one object context find learning time distributed exponentially learning rate given lnfracNN1C N12 case N target word sampled randomly frac1N ln fracN1C case follow deterministic presentation sequence learning performance much superior exhibited human realistic learning algorithm crosssituational experiment show introduction discrimination limitation using Webers law forgetting reduce performance associative algorithm human level Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shalini Puri', 'Sona Kaushik']

1204.2058v1

In this new and current era of technology, advancements and techniques, efficient and effective text document classification is becoming a challenging and highly required area to capably categorize text documents into mutually exclusive categories. Fuzzy similarity provides a way to find the similarity of features among various documents. In this paper, a technical review on various fuzzy similarity based models is given. These models are discussed and compared to frame out their use and necessity. A tour of different methodologies is provided which is based upon fuzzy similarity related concerns. It shows that how text and web documents are categorized efficiently into different categories. Various experimental results of these models are also discussed. The technical comparisons among each model's parameters are shown in the form of a 3-D chart. Such study and technical review provide a strong base of research work done on fuzzy similarity based text document categorization.

A technical study and analysis on fuzzy similarity based models for text classification

http://arxiv.org/pdf/1204.2058v1

Title technical study analysis fuzzy similarity based model text classification Summary new current era technology advancement technique efficient effective text document classification becoming challenging highly required area capably categorize text document mutually exclusive category Fuzzy similarity provides way find similarity feature among various document paper technical review various fuzzy similarity based model given model discussed compared frame use necessity tour different methodology provided based upon fuzzy similarity related concern show text web document categorized efficiently different category Various experimental result model also discussed technical comparison among model parameter shown form 3D chart study technical review provide strong base research work done fuzzy similarity based text document categorization Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Shalini Puri']

1204.2061v1

Text Classification is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification algorithms. In this paper, a new Fuzzy Similarity Based Concept Mining Model (FSCMM) is proposed to classify a set of text documents into pre - defined Category Groups (CG) by providing them training and preparing on the sentence, document and integrated corpora levels along with feature reduction, ambiguity removal on each level to achieve high system performance. Fuzzy Feature Category Similarity Analyzer (FFCSA) is used to analyze each extracted feature of Integrated Corpora Feature Vector (ICFV) with the corresponding categories or classes. This model uses Support Vector Machine Classifier (SVMC) to classify correctly the training data patterns into two groups; i. e., + 1 and - 1, thereby producing accurate and correct results. The proposed model works efficiently and effectively with great performance and high - accuracy results.

A Fuzzy Similarity Based Concept Mining Model for Text Classification

http://arxiv.org/pdf/1204.2061v1

Title Fuzzy Similarity Based Concept Mining Model Text Classification Summary Text Classification challenging red hot field current scenario great importance text categorization application lot research work done field need categorize collection text document mutually exclusive category extracting concept feature using supervised learning paradigm different classification algorithm paper new Fuzzy Similarity Based Concept Mining Model FSCMM proposed classify set text document pre defined Category Groups CG providing training preparing sentence document integrated corpus level along feature reduction ambiguity removal level achieve high system performance Fuzzy Feature Category Similarity Analyzer FFCSA used analyze extracted feature Integrated Corpora Feature Vector ICFV corresponding category class model us Support Vector Machine Classifier SVMC classify correctly training data pattern two group e 1 1 thereby producing accurate correct result proposed model work efficiently effectively great performance high accuracy result Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Valentina Fedorova', 'Alex Gammerman', 'Ilia Nouretdinov', 'Vladimir Vovk']

1204.3251v2

A standard assumption in machine learning is the exchangeability of data, which is equivalent to assuming that the examples are generated from the same probability distribution independently. This paper is devoted to testing the assumption of exchangeability on-line: the examples arrive one by one, and after receiving each example we would like to have a valid measure of the degree to which the assumption of exchangeability has been falsified. Such measures are provided by exchangeability martingales. We extend known techniques for constructing exchangeability martingales and show that our new method is competitive with the martingales introduced before. Finally we investigate the performance of our testing method on two benchmark datasets, USPS and Statlog Satellite data; for the former, the known techniques give satisfactory results, but for the latter our new more flexible method becomes necessary.

Plug-in martingales for testing exchangeability on-line

http://arxiv.org/pdf/1204.3251v2

Title Plugin martingale testing exchangeability online Summary standard assumption machine learning exchangeability data equivalent assuming example generated probability distribution independently paper devoted testing assumption exchangeability online example arrive one one receiving example would like valid measure degree assumption exchangeability falsified measure provided exchangeability martingale extend known technique constructing exchangeability martingale show new method competitive martingale introduced Finally investigate performance testing method two benchmark datasets USPS Statlog Satellite data former known technique give satisfactory result latter new flexible method becomes necessary Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Maria-Florina Balcan', 'Avrim Blum', 'Shai Fine', 'Yishay Mansour']

1204.3514v3

We consider the problem of PAC-learning from distributed data and analyze fundamental communication complexity questions involved. We provide general upper and lower bounds on the amount of communication needed to learn well, showing that in addition to VC-dimension and covering number, quantities such as the teaching-dimension and mistake-bound of a class play an important role. We also present tight results for a number of common concept classes including conjunctions, parity functions, and decision lists. For linear separators, we show that for non-concentrated distributions, we can use a version of the Perceptron algorithm to learn with much less communication than the number of updates given by the usual margin bound. We also show how boosting can be performed in a generic manner in the distributed setting to achieve communication with only logarithmic dependence on 1/epsilon for any concept class, and demonstrate how recent work on agnostic learning from class-conditional queries can be used to achieve low communication in agnostic settings as well. We additionally present an analysis of privacy, considering both differential privacy and a notion of distributional privacy that is especially appealing in this context.

Distributed Learning, Communication Complexity and Privacy

http://arxiv.org/pdf/1204.3514v3

Title Distributed Learning Communication Complexity Privacy Summary consider problem PAClearning distributed data analyze fundamental communication complexity question involved provide general upper lower bound amount communication needed learn well showing addition VCdimension covering number quantity teachingdimension mistakebound class play important role also present tight result number common concept class including conjunction parity function decision list linear separator show nonconcentrated distribution use version Perceptron algorithm learn much le communication number update given usual margin bound also show boosting performed generic manner distributed setting achieve communication logarithmic dependence 1epsilon concept class demonstrate recent work agnostic learning classconditional query used achieve low communication agnostic setting well additionally present analysis privacy considering differential privacy notion distributional privacy especially appealing context Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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['Seyda Ertekin', 'Haym Hirsh', 'Cynthia Rudin']

1204.3611v1

The problem of "approximating the crowd" is that of estimating the crowd's majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algorithm, "CrowdSense," that works in an online fashion to dynamically sample subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of a subset of the labelers' votes that approximates the crowd's opinion.

Learning to Predict the Wisdom of Crowds

http://arxiv.org/pdf/1204.3611v1

Title Learning Predict Wisdom Crowds Summary problem approximating crowd estimating crowd majority opinion querying subset Algorithms approximate crowd intelligently stretch limited budget crowdsourcing task present algorithm CrowdSense work online fashion dynamically sample subset labelers based explorationexploitation criterion algorithm produce weighted combination subset labelers vote approximates crowd opinion Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Tesau 3 Sebastian Ruder Joachim Bingel Isabelle Aug 4 Iulian V Serban Chinnadhurai Sankar Mathie 40995 Vitaly Feldman Pravesh Kothari Jan Vondrák 40996 Orly Avner Shie Mannor 40997 Ran Zhao Deanna Needell Christopher Johanse 40998 Zongyan Huang Matthew England David Wilson 40999 Imen Trabelsi Dorra Ben Ayed Name author Length 41000 dtype object

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