Paris Noah's Ark Lab
Projects
Preprints
Large Language Models as Markov Chains
: theoretical insights on their generalization and convergence properties.
A systematic study comparing hyperparameter optimization engines on tabular data
2024
(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
2023
(ICML '23)
Meta Optimal Transport
(AAAI '23)
Unbalanced Co-Optimal Transport
(ICML '23)
Multi-Agent Best Arm Identification with Private Communications
(ICML '23)
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
(ICML '23)
PCA-based Multi Task Learning: a Random Matrix Approach