Spaces:
Running
Running
File size: 2,862 Bytes
3117bf6 b297dbb a478593 5075e2a b297dbb f929720 a478593 f8a4912 f432111 f8a4912 d7e55f8 f929720 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 |
---
title: README
emoji: 🏃
colorFrom: indigo
colorTo: gray
sdk: static
pinned: false
---
# Paris Noah's Ark Lab
## Projects
### Preprints
- [Zero-shot Model-based Reinforcement Learning using Large Language Models](https://huggingface.co/papers/2410.11711): disentangled in-context learning for multivariate time series forecasting with LLMs, and an application to model-based reinforcement learning.
- [Large Language Models as Markov Chains](https://huggingface.co/papers/2410.02724): theoretical insights on their generalization and convergence properties.
- [A systematic study comparing hyperparameter optimization engines on tabular data](https://balazskegl.medium.com/navigating-the-maze-of-hyperparameter-optimization-insights-from-a-systematic-study-6019675ea96c)
### 2024
- *(NeurIPS'24)* [MANO: Unsupervised Accuracy Estimation Under Distribution Shifts](https://huggingface.co/papers/2405.18979): when logits are enough to estimate generalization of a pre-trained model.
- *(NeurIPS'24, **Spotlight**)* [Analysing Multi-Task Regression via Random Matrix Theory](https://arxiv.org/pdf/2406.10327): insights on a classical approach and its potentiality for time series forecasting.
- *(ICML'24, **Oral**)* [SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting](https://huggingface.co/papers/2402.10198): sharpness-aware minimization and channel-wise attention is all you need.
- *(AISTATS'24)* [Leveraging Ensemble Diversity for Robust Self-Training](https://huggingface.co/papers/2310.14814): confidence estimation method for efficient pseudo-labeling under sample selection bias.
- *(JMLR, 2024)* [Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data](https://www.jmlr.org/papers/volume25/23-0121/23-0121.pdf) generalization with unlabeled or pseudo-labeled data.
- *(ICML '24)* [Position: A Call for Embodied AI](https://arxiv.org/abs/2402.03824): position paper on the need for embodied AI research
- *(RLC '24)* [A Study of the Weighted Multi-step Loss Impact on the Predictive Error and the Return in MBRL](https://openreview.net/pdf?id=K4VjW7evSV): multi-step loss in MBRL does not work as well as expected
### 2023
- *(ICML '23)* [Meta Optimal Transport](https://arxiv.org/abs/2206.05262)
- *(AAAI '23)* [Unbalanced Co-Optimal Transport](https://arxiv.org/abs/2205.14923)
- *(ICML '23)* [Multi-Agent Best Arm Identification with Private Communications](https://proceedings.mlr.press/v202/rio23a.html)
- *(ICML '23)* [Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption](https://proceedings.mlr.press/v202/feofanov23a.html)
- *(ICML '23)* [PCA-based Multi Task Learning: a Random Matrix Approach](https://proceedings.mlr.press/v202/tiomoko23a/tiomoko23a.pdf)
|