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**A**: (2005) preprint has a typo, which in the published version has been fixed by redefining the meaning of q𝑞qitalic_q just for this equation, but we stick with the more natural definition of q𝑞qitalic_q and rewrite the equation. To get the corresponding approximation for the mean, we simply divide by S𝑆Sitalic_S to get
**B**: (2005, Eq. 28) provide a closed form approximation of quantile q>0.95𝑞0.95q>0.95italic_q > 0.95 for the sum of Pareto-distributed variables.333The Zaliapin et al**C**: Zaliapin et al | ACB | BAC | ABC | CBA | Selection 4 |
**A**: To enhance the visualization, we exclude street segments with wealth estimates greater than the 95th percentile ($6,272,010)**B**: **C**:
Figure 2: Counts of residential burglary (slightly jittered) versus wealth estimate by neighborhood for each street segment; illustrates the nonconstant effects of wealth on crime | BCA | CBA | CBA | CBA | Selection 1 |
**A**: For each threshold, we select the response variables whose absolute effect sizes are greater than the threshold**B**: If the selected explanatory variable has value above the threshold in ground truth effect size, it will be the true positive..
**C**: In this section, we evaluate the yielded results of the TgSLMM versus Tree-Lasso, LMM-Lasso and some techniques mentioned above, which is shown in the receiver operating characteristic (ROC) curves 111The problem can be regarded as classification problem–identifying the active response variables from all genes | ACB | BAC | BCA | BAC | Selection 3 |
**A**: The only difference happens to patient 10 and 12 whose intakes are earlier at day.
Further, patient 12 takse approx**B**: 3 times the average insulin dose of others in the morning.**C**: The insulin intakes tend to be more in the evening, when basal insulin is used by most of the patients | BCA | ABC | CAB | CBA | Selection 1 |
**A**: However, their combined approach is not appropriate in a SEM setting, as their approach performs the model averaging over both the first stage regression (corresponding to instruments) and the second stage regression (corresponding to the structural model). In a traditional instrumental variable regression setting, the instruments are auxillary to the predictor variables, making it possible to apply the model averaging in two stages as the set of instrumental variables is not determined by the selection of predictor variables.
**B**: This procedure addresses uncertainty in both the selection of instruments as well as the combination of endogenous and exogenous predictors of the targeted outcome. This differs from applying BMA to a multiple regression model, as their approach averages over both first and second stage models, and weights the second stage coefficients by the product of both the first and second stage models’ probability**C**: Lenkoski \BOthers. (\APACyear2014) applied BMA to both the first and second stage of 2SLS in regression models without latent variables | CBA | BCA | CAB | ABC | Selection 1 |
**A**: Next, three deconvolutional layers of 64646464 filters follow. An additional deconvolutional layer outputs an image of the original 105×8010580105\times 80105 × 80 size. The number of filters is either 3333 or 3×25632563\times 2563 × 256**B**: In our experiments, we varied details of the architecture above. In most cases, we use a stack of four convolutional layers with 64646464 filters followed by three dense layers (the first two have 1024102410241024 neurons). The dense layers are concatenated with 64646464 dimensional vector with a learnable action embedding**C**: In the first case, the output is a real-valued approximation of pixel’s RGB value. In the second case, filters are followed by softmax producing a probability distribution on the color space. The reward is predicted by a softmax attached to the last fully connected layer.
We used dropout equal to 0.20.20.20.2 and layer normalization. | BAC | ACB | ACB | ABC | Selection 1 |
**A**: Dupuy [12] discussed about different weapon system in his book about the Evolution of Weapons and Warfare (1990) which had evolved from 2000 BCE onwards till the Cold War and their tactical impact on combat**B**: Despite its Western bias, the book is good for detailed description of the military hardware which modern Europe produced. Eminent author K Roy [33] described a global history of warfare from slings to drones also includes discussion on insurgency, civil war, sieges, skirmishes, ambushes and raids.**C**:
US Army’s Colonel Trevor N | ABC | CBA | CAB | BCA | Selection 4 |
**A**: Note that we impose a constraint on the momentum coefficient β𝛽\betaitalic_β during the theoretical proof**B**: But in practice, even when the constraint is relaxed, e.g., β=0.9𝛽0.9\beta=0.9italic_β = 0.9,
GMC still converges well**C**: More details about the convergence performance of GMC are provided in Section 5. | BCA | ABC | ACB | BAC | Selection 2 |
**A**: operation.**B**:
, where ∗*∗ is the convolution333We use convolution instead of cross-correlation only as a matter of compatibility with previous literature and computational frameworks**C**: Using cross-correlation would produce the same results and would not require flipping the kernels during visualization | ACB | ACB | ABC | CAB | Selection 4 |
**A**: Operationally, the residual permutation test compares the observed value of the test statistic with values of the statistic calculated at randomly permuted residuals**B**: However, the validity conditions for the
residual permutation test vary substantially depending on the hypothesis regime, as shown in the theorem below.**C**: This leads to a randomization-based analogue of the residual bootstrap procedure of Freedman (1981) | CAB | BCA | ABC | ACB | Selection 4 |
**A**: When volunteers attempt to donate, their hemoglobin levels are measured. If the h-level is below an eligibility threshold (see Table 3), the donor is ineligible to donate blood and receives a temporary deferral. Thus, the h-level threshold provides a natural experiment to identify the causal effect of the temporary deferral using a regression discontinuity design.
**B**: We now investigate whether it is causal by exploiting a discontinuity in the blood donor’s eligibility criteria**C**: Having shown a negative correlation between deferrals on return behavior | ACB | ABC | CBA | BAC | Selection 3 |
**A**:
A fully connected neural network architecture was used**B**: It was composed of two hidden layers of 128 neurons and two Dropout layers between the input layer and the first hidden layer and between the two hidden layers**C**: ADAM optimizer for the minimization[25]. | CBA | ABC | CBA | BCA | Selection 2 |
**A**: First, we analyze the performance of state-of-the-art methods for mapping random forests into neural networks and neural random forest imitation**B**: That means that the methods aim for the lower-left corner (smaller number of network parameters and higher accuracy). Please note that the y-axis is shown on a logarithmic scale.**C**: The results are shown in Figure 4 for different numbers of training examples per class.
For each method, the average number of parameters of the generated networks across all datasets is plotted depending on the test error | BCA | ACB | ABC | BAC | Selection 2 |
**A**: We refer to the introduction of the latter article for further**B**: SBM and OBM and their local time have been recently investigated in the context of option pricing, as for instance in [20] and [16].
In [37] it is shown that a time series of threshold diffusion type captures leverage and mean-reverting effects**C**: Some models in financial mathematics and econometrics are threshold diffusions, for instance continuous-time versions of SETAR (self-exciting threshold auto-regressive) models, see e.g. [15, 41] | BAC | BCA | CBA | BAC | Selection 3 |
**A**: As is shown subsequently, solving such a subproblem corresponds to one iteration of infinite-dimensional mirror descent (Nemirovsky and Yudin, 1983) or dual averaging (Xiao, 2010), where the action-value function plays the role of the gradient. To encourage exploration, we explicitly incorporate a bonus function into the action-value function, which quantifies the uncertainty that arises from only observing finite historical data. Through uncertainty quantification, such a bonus function ensures the (conservative) optimism of the updated policy. Based on NPG, TRPO, and PPO, OPPO only augments the action-value function with the bonus function in an additive manner, which makes it easily implementable in practice.
**B**: Our algorithm is also closely related to NPG and TRPO. At each update, OPPO solves a Kullback-Leibler (KL)-regularized policy optimization subproblem, where the linear component of the objective function is defined using the action-value function**C**: To answer this question, we propose the first policy optimization algorithm that incorporates exploration in a principled manner. In detail, we develop an Optimistic variant of the PPO algorithm, namely OPPO | ACB | CBA | BAC | BAC | Selection 2 |
**A**: Sparse attention mechanisms and approximations have been proposed to address this issue and improve the efficiency of transformers for longer sequences.
We refer to the work of Tay et al**B**: (2022) which provides an overview of various transformer-based architectures that focus on efficiency, reduced memory-footprint and computational complexity**C**: Most of these methods focus on the quadratic complexity of the self-attention heads and use low-rank matrix operations, downsampling or exploit pre-set or learned sparsity patterns. | CAB | BCA | ABC | CBA | Selection 3 |
**A**: The high density of benign cases (c) seems to indicate that their high-dimensional profile is clearer and less diverse than malignant cases, which are more sparse. Different combinations of dimensions are correlated with patterns between clusters (c, d) and inside clusters (e, f), which affects the interpretation of clusters. The investigation of outliers leads to identifying points that are hard to classify due to class mixing (g) and groups with identical dimension values (h).**B**: The Overview (a) and the Shepard Heatmap (b) indicate that the overall accuracy is good**C**:
Figure 6: Usage scenario based on the Breast Cancer Wisconsin data set | BCA | BAC | ACB | CBA | Selection 4 |
**A**: All codes are downloaded from the homepages of authors.
**B**: Besides, four GAE-based methods are used, including GAE [20], MGAE [21], GALA [32], and SDCN [31]**C**: Three deep clustering methods for general data, DEC [8] DFKM [9], and SpectralNet [7], also serve as an important baseline | BCA | BAC | ABC | CBA | Selection 4 |
**A**: This is a two-step estimator with tuning parameters for kernel estimation and sieves estimation, such as the bandwidth and penalization levels, which must be chosen by cross-validation. Because of the local structure of the hybrid estimator, the framework
of Lu et al. (2020) differs from ours in that they consider an additive local approximation model with sparsity (ATLAS), in which they need to impose a local sparsity structure.**B**: A procedure explicitly addressing the construction of uniformly valid confidence bands for the components in high-dimensional additive models has been developed by Lu et al. (2020). The authors emphasize that achieving uniformly valid inference in these models is challenging due to the difficulty of directly generalizing the ideas from the fixed-dimensional case**C**: Whereas confidence bands in the low-dimensional case are mostly built using kernel methods, the estimators for high-dimensional sparse additive models typically rely on sieve estimators based on dictionaries. To derive their results, Lu et al. (2020) combine both kernel and sieve methods to draw upon the advantages of each, resulting in a kernel-sieve hybrid estimator | CAB | BAC | BAC | CBA | Selection 1 |
**A**: Next, we perform similar steps for RF vs. ExtraT without class optimization as shown in Figure 2(a.2, d).**B**: A drawback is the complexity of it compared to multiple simpler scatterplots.
Figure 2(c.1) indicates that, after the parameter tuning, the selected KNN models (narrow, more saturated bars) perform better than the average (wide, less saturated bars) and are thus good picks for our ensemble**C**: Wang et al. [62] experimented with alternative visualization designs for selecting parameters, and they found that a parallel coordinates plot is a solid representation for this context as it is concise and also not rejected by the users | BCA | BAC | ACB | CBA | Selection 4 |
**A**: (2018, 2019), the PDE in (3.4) can not be cast as a gradient flow, since there does not exist a corresponding energy functional**B**: The proof of Proposition 3.1 is based on the propagation of chaos (Sznitman, 1991; Mei et al., 2018, 2019).
In contrast to Mei et al**C**: Thus, their analysis is not directly applicable to our setting. We defer the detailed discussion on the approximation analysis to §B. Proposition 3.1 allows us to convert the TD dynamics over the finite-dimensional parameter space to its counterpart over the infinite-dimensional Wasserstein space, where the infinitely wide neural network Q(⋅;ρ)𝑄⋅𝜌Q(\cdot;\rho)italic_Q ( ⋅ ; italic_ρ ) in (3.2) is linear in the distribution ρ𝜌\rhoitalic_ρ. | ABC | ABC | BAC | CBA | Selection 3 |
**A**: For this paper we focus on \chCO2 emissions as the main output of an ensemble of coupled climate-economy-energy models. Each model-scenario produces a vector of \chCO2 emissions defined from the year 2010 to 2090 at 10-years time intervals**B**: This discretization of the output space is in any case arbitrary, since \chCO2 emissions do exist in every time instant in the interval T=[2010,2090]𝑇20102090T=[2010,2090]italic_T = [ 2010 , 2090 ]. A thorough description of the dataset used as a testbed for the application of the methods described before can be found in [17]**C**: This was one of the first paper to apply global sensitivity techniques to an ensemble of climate economy models, thus addressing both parametric and model uncertainty. We use the scenarios developed in [17] which involve five models (IMAGE, IMACLIM, MESSAGE-GLOBIOM, TIAM-UCL and WITCH-GLOBIOM) that provide output data until the end of the interval T𝑇Titalic_T.
| ABC | BCA | ACB | ACB | Selection 1 |
**A**: Chapter 11 of Fan et al**B**: (2020) and the references therein provide a thorough review of recent advances and applications of multivariate factor models.
For 2nd-order tensor (or matrix) data, Wang et al**C**: (2019); Chen et al. (2019, 2020b) consider the matrix factor model which is a special case of (1) with M=2𝑀2M=2italic_M = 2 and propose estimation procedures based on the second moments. | ABC | BCA | CBA | BAC | Selection 1 |
**A**:
To further verify the superiority of SNGM with respect to LARS, we also evaluate them on a larger dataset ImageNet [2] and a larger model ResNet50 [10]**B**: We train the model with 90 epochs**C**: As recommended in [32], we use warm-up and polynomial learning rate strategy. | ACB | CBA | BAC | ABC | Selection 4 |
**A**: (2010).
Recall that we recommend the choice of commonly used cubic splines (i.e., ζ=4𝜁4\zeta=4italic_ζ = 4) in Section 3 to implement our method when prior information about the Hölder smoothness condition of the broadcasted functions is unavailable.**B**: (1998) and Huang et al**C**: Despite this mild difference in parameter identification, similar assumptions can be found in Zhou et al | ACB | BAC | BCA | CBA | Selection 4 |
**A**: The instantaneous reward is the payoff when viewers are redirected to an advertiser, and the state is defined as the the details of the advertisement and user contexts. If the target users’ preferences are time-varying, time-invariant reward and transition function are unable to capture the dynamics**B**: In general nonstationary random processes naturally occur in many settings and are able to characterize larger classes of problems of interest (Cover & Pombra, 1989). Can one design a theoretically sound algorithm for large-scale nonstationary MDPs? In general it is impossible to design algorithm to achieve sublinear regret for MDPs with non-oblivious adversarial reward and transition functions in the worst case (Yu et al., 2009). Then what is the maximum nonstationarity a learner can tolerate to adapt to the time-varying dynamics of an MDP with potentially infinite number of states? This paper addresses these two questions.**C**:
However, all of the aforementioned empirical and theoretical works on RL with function approximation assume the environment is stationary, which is insufficient to model problems with time-varying dynamics. For example, consider online advertising | BAC | BCA | ABC | CBA | Selection 2 |
**A**: The key observation that we make is that the DR learning problem can be cast as a style transfer task [DBLP:conf/cvpr/GatysEB16], thus allowing us to borrow techniques from this extensively explored area.
**B**: Furthermore, even though it involves two stages, the end result is a single model which does not rely on any auxiliary models, additional hyper-parameters, or hand-crafted loss functions, as opposed to previous works addressing the problem (see Section LABEL:sec:related for a survey of related work)**C**: The framework is general and can utilize any DGM | BAC | BAC | ACB | CBA | Selection 4 |
**A**: Nonnegative ridge regression is followed by the elastic net and then lasso. The lasso is followed by the adaptive lasso, NNFS and stability selection, although the order among these three methods changes somewhat for the different conditions**B**:
The true positive rate in view selection for each of the meta-learners can be observed in Figure 2. Ignoring the interpolating predictor for now, nonnegative ridge regression has the highest TPR, which is unsurprising seeing as it performs feature selection only through its nonnegativity constraints**C**: The interpolating predictor shows behavior that is completely different from the other meta-learners. Whereas for the other meta-learners the TPR increases as sample size increases, the TPR of the interpolating predictor actually decreases in some cases. Although it appears to have the highest TPR in some conditions, it can be observed in the next section that it also has the highest FPR in these conditions. | CBA | BAC | ACB | ACB | Selection 2 |
**A**:
CB-MNL enforces optimism via an optimistic parameter search (e.g. in Abbasi-Yadkori et al**B**: [2011]), which is in contrast to the use of an exploration bonus as seen in Faury et al. [2020], Filippi et al**C**: [2010]. Optimistic parameter search provides a cleaner description of the learning strategy. In non-linear reward models, both approaches may not follow similar trajectory but may have overlapping analysis styles (see Filippi et al. [2010] for a short discussion). | BAC | CBA | BAC | ABC | Selection 4 |
**A**: Nevertheless, he noticed that in evolutionary optimization, hundreds of stages might not be necessary since, with three stages, we could gather performant models that are hard to surpass in terms of predictive performance. Finally, E1 mentioned that controlling the evolutionary process via the Sankey diagram can be time-saving.
**B**: E2 has recently worked with genetic algorithms for testing traffic-scenarios for autonomous vehicles. In that case, they had to set a strict budget before execution and perform multiple crossover and mutation stages which can take days to run**C**: E1 and E2 commented that the workflow of VisEvol is well designed. Although E3 expected a more linear workflow, she agreed that the combined views are better positioned at the top, with the interactive projections in the middle and the shared views at the bottom | BAC | CBA | BCA | CAB | Selection 2 |
**A**: To overcome this shortcoming, mixedSCORE proposed a degree-corrected mixed membership (DCMM) model. DCMM model allows that nodes for the same communities have different degrees and some nodes could belong to two or more communities, thus it is more realistic and flexible. In this paper, we design community detection algorithms based on the DCMM model.**B**: DCSBM is widely used for community detection for non-mixed membership networks (zhao2012consistency, ; SCORE, ; cai2015robust, ; chen2018convexified, ; chen2018network, ; ma2021determining, ). MMSB constructed a mixed membership stochastic blockmodel (MMSB) which is an extension of SBM by letting each node have different weights of membership in all communities. However, in MMSB, nodes in the same communities still share the same degrees**C**:
The stochastic blockmodel (SBM) (SBM, ) is one of the most used models for community detection in which all nodes in the same community are assumed to have equal expected degrees. Some recent developments of SBM can be found in (abbe2017community, ) and references therein. Since in empirical network data sets, the degree distributions are often highly inhomogeneous across nodes, a natural extension of SBM is proposed: the degree-corrected stochastic block model (DCSBM) (DCSBM, ) which allows the existence of degree heterogeneity within communities | BCA | ABC | CBA | ABC | Selection 3 |
**A**: (2018); Boumal et al. (2018); Bécigneul and Ganea (2018); Zhang and Sra (2018); Sato et al. (2019); Zhou et al. (2019); Weber and Sra (2019) and the references therein.
Also see recent reviews (Ferreira et al., 2020; Hosseini and Sra, 2020)**B**: (2017); Agarwal et al. (2018); Zhang et al. (2018); Tripuraneni et al**C**: See, e.g., Udriste (1994); Ferreira and Oliveira (2002); Absil et al. (2009); Ring and Wirth (2012); Bonnabel (2013); Zhang and Sra (2016); Zhang et al. (2016); Liu et al | CBA | BCA | ABC | ABC | Selection 1 |
**A**: Statistical measures such as target correlation and mutual information shared between features, along with per class correlation, are necessary to evaluate the features’ influences in the result**B**: Also, the tool should use variance influence factor and in-between features’ correlation for identifying colinearity issues. When checking how to modify features, users should be able to estimate the impact of such transformations.**C**: G3: Application of alternative feature transformations according to feedback received from statistical measures.
In continuation of the preceding goal, the tool should provide sufficient visual guidance to users to choose between diverse feature transformations (T3) | ABC | BCA | CAB | ABC | Selection 2 |
**A**: These methods can be grouped into two types: 1) those that assume the bias variables e.g., the gender label in CelebA, are explicitly annotated and can be accessed during training [55, 55, 69, 37] and, 2) those that do not require explicit access [46, 50]**B**:
Recently, many methods have been proposed to make neural networks bias resistant**C**: Assuming explicit access requires extra annotations in addition to the actual target, and for many tasks it may not be immediately clear what the bias variables are e.g., biases may only be discovered years later [51, 50]. Methods that do not assume access to these bias variables have only recently been proposed [46, 65, 50]. | ABC | CAB | BAC | BCA | Selection 3 |
**A**: Then, in Section 2 we introduce GP emulators. Section 3 reviews RFF and its application to kernel approximation and GPs**B**: Section Section 4 describes our proposed method for emulating dynamical models. Numerical results are provided in Section 5 where we apply our method to emulate several dynamical systems. Finally, Section 7 presents our conclusions.
**C**: The rest of the paper is organised as follows. First, we give a brief overview of dynamical systems | BAC | BCA | CAB | ACB | Selection 2 |
**A**: This makes possible the nonparametric inference under heavy-tailed data-generating distributions such as stable laws Yang (2012) and Pareto distributions Rizzo (2009), and it distinguishes our tests from commonly used techniques like traditional distance covariance and energy statistic, for more discussion, refer to Deb and Sen (2023).
**B**: Consistency under absolute continuity**C**: The unique condition for the consistency of our tests is that the underlying distributions are absolutely continuous, without the need for any moment requirements | CBA | BCA | CAB | CBA | Selection 3 |
**A**: [2022] is in essence the Frank-Wolfe algorithm with a modified version of the backtracking line search of Pedregosa et al**B**: We note that the LBTFW-GSC algorithm from Dvurechensky et al**C**: [2020]. In the next section, we provide improved convergence guarantees for various cases of interest for this algorithm, which we refer to as the Frank-Wolfe algorithm with Backtrack (B-FW) for simplicity.
| ACB | ACB | ABC | BAC | Selection 4 |
**A**:
An alternative route for avoiding the dependence on worst case queries and datasets was achieved using expectation based stability notions such as mutual information and KL stability Russo and Zou (2016); Bassily et al**B**: (2021); Steinke and Zakynthinou (2020)**C**: Using these methods Feldman and Steinke (2018) presented a natural noise addition mechanism, which adds noise that scales with the empirical variance when responding to queries with known range and unknown variance. Unfortunately, in the general case, the accuracy guarantees provided by these methods hold only for the expected error rather than with high probability. | ACB | CAB | ABC | CAB | Selection 3 |
**A**: As shown in barber2021predictive , these methods always produce confidence regions that are contained in the intervals coming from jackknife+ estimators, with the added complexity that they are not necessarily connected, i.e. they might be disjoint unions of intervals.
**B**: Similar to how the interval estimator (17) for bagged ensembles could be extended to a general cross-validation inspired framework barber2021predictive , the above approach for conformal prediction can also be extended to a framework in which the full data set is used to calibrate the model through a k𝑘kitalic_k-fold or leave-one-out approach vovk2015cross **C**: For this reason they are called cross-conformal predictors | ACB | CBA | CAB | ACB | Selection 3 |
**A**: Overall reciprocity improves outcomes in the baseline condition but can backfire substantially due to negative reciprocity in the treatment condition. However, a uniform increase in subjects’ positive reciprocity attribute has a substantial positive effect on all key outcomes in both the treatment and baseline conditions.
**B**: Consistent with our estimates for the model with individual heterogeneity, an increase in the trust attribute improves key outcomes in the information treatment, but has a mild, negative impact on the baseline condition**C**: Finally, we use our structural framework to conduct three counterfactual simulations, each examining the effects of a uniform increase in one of the three principal attributes—trust, overall reciprocity, and positive reciprocity | CBA | CAB | ACB | CAB | Selection 1 |
**A**: Raster plot exhibiting the spectral density estimates for each of the 441 grid squares for the period from November 04, 2013, to November 11, 2013**B**: The x𝑥xitalic_x-axis only exhibits frequencies up to 0.05. The black vertical lines represent the frequencies that were chosen to be included in the harmonic regression after visual inspection of this graph.**C**:
Figure 4 | ABC | ACB | BCA | ACB | Selection 3 |
**A**: However, for the exposition in this section it sufficient to know what the properties of the operators 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are.
**B**: This process is somewhat elaborate and the reader is referred to [31] and [32] for all of the details**C**: The operator 𝐋𝐋\mathbf{L}bold_L and 𝐖𝐖\mathbf{W}bold_W are constructed from a multilevel decomposition of the location of predictors | BCA | CBA | BAC | CAB | Selection 2 |
**A**:
Another way to obtain (1) is given in the next proposition**B**: The proof of the next proposition is deferred to the end of the Appendix, Section 10.**C**: It requires the existence of a dominating measure for which a standard bracketing entropy condition is satisfied | ABC | BCA | CAB | ACB | Selection 4 |
**A**: We remark that this example is just for illustration and showcasing the interpretation of the proposed tensor factor model**B**: In Chen et al., (2022), varimax rotation was used to find the most sparse loading matrix representation to model interpretation. For TFM-cp, the model is unique hence interpretation can be made directly. Interpretation is impossible for the vector factor model in such a high dimensional case.
**C**: Again we note that for the TFM-tucker model, one needs to identify a proper representation of the loading space in order to interpret the model | ACB | BAC | CAB | BCA | Selection 1 |
**A**: On the one hand, their trust in such decisions could be low due to a lack of in-depth knowledge on how models are learning from the training data. On the other hand, ML experts often have little prior knowledge about the data from particular domains**B**:
In the InfoVis/VA communities, most of the research in explainable ML focuses on assisting ML experts and developers in understanding, debugging, refining, and comparing ML models. Chatzimparmpas2020A ; Chatzimparmpas2020The In this paper, we expand our method to involve another target group: the various domain experts affected by the ML progress in fields such as finance, social care, and health care. With the growing adoption of ML in different areas, domain experts with little knowledge of ML algorithms might still want (or be required) to use them to assist in their decision-making**C**: Thus, the primary goal of VisRuler is to combine the best of both worlds, i.e., to offer a solution that combines the above-mentioned benefits from both expert groups. More details about the collaboration between the ML and domain experts can be found in Section System Overview and Use Case. | BAC | ACB | BCA | CAB | Selection 1 |
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