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Paris Noah's Ark Lab

Projects

Zero-shot Model-based Reinforcement Learning using Large Language Models: 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: theoretical insights on their generalization and convergence properties.
A systematic study comparing hyperparameter optimization engines on tabular data

(NeurIPS'24) MANO: Unsupervised Accuracy Estimation Under Distribution Shifts: when logits are enough to estimate generalization of a pre-trained model.
(NeurIPS'24, Spotlight) Analysing Multi-Task Regression via Random Matrix Theory: 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: sharpness-aware minimization and channel-wise attention is all you need.
(AISTATS'24) Leveraging Ensemble Diversity for Robust Self-Training: 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 generalization with unlabeled or pseudo-labeled data.
(ICML '24) Position: A Call for Embodied AI: 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: multi-step loss in MBRL does not work as well as expected