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Paris Noah's Ark Lab

We are a research laboratory located in Paris, working on the fundamental and practical development of modern artificial intelligence systems. Paris Noah's Ark Lab consists of 3 research teams that cover the following topics:

Time Series and Transfer Learning (Team leader: Ievgen Redko)
Auto-Data-Science and Reinforcement Learning (Team leader: Balazs Kegl)
Autonomous Driving

Projects

Preprints

Zero-shot Model-based Reinforcement Learning using Large Language Models: disentangled in-context learning for multivariate time series forecasting and model-based RL.
Large Language Models as Markov Chains: theoretical insights on their generalization and convergence properties.
A Systematic Study Comparing Hyperparameter Optimization Engines on Tabular Data: insights to navigate the maze of hyperopt techniques.

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