7.1k citations
- Centre National de la Recherche ScientifiqueFR310 papers
- Centre de Recherche en Informatique, Signal et Automatique de LilleFR122 papers
- Laboratoire Paul PainlevéFR114 papers
- Sorbonne UniversitéFR98 papers
- École Centrale de LilleFR97 papers
- Centre de Recherche en InformatiqueFR90 papers
- Laboratoire de Physique des PlasmasFR66 papers
- Institut d'électronique de microélectronique et de nanotechnologieFR65 papers
- Université Paris Sciences et LettresFR65 papers
- Université Paris CitéFR56 papers
- Institut de Mécanique Céleste et de Calcul des ÉphéméridesFR50 papers
- Laboratoire de Physique des Lasers, Atomes et MoléculesFR46 papers
26 papers · 1 filter
Dimension Agnostic Testing of Survey Data Credibility through the Lens of Regression
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda +3
Assessing whether a sample survey credibly represents the population is a critical question for ensuring the validity of downstream research. Generally, this problem reduces to est…
Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learning
Marc Damie, Edwige Cyffers
Decentralized machine learning - where each client keeps its own data locally and uses its own computational resources to collaboratively train a model by exchanging peer-to-peer m…
The regret lower bound for communicating Markov Decision Processes
Victor Boone, Odalric-Ambrym Maillard
This paper is devoted to the extension of the regret lower bound beyond ergodic Markov decision processes (MDPs) in the problem dependent setting. While the regret lower bound for…
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret
Emilio Jorge, Christos Dimitrakakis, Debabrota Basu
Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions…
Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of Factored-POMDPs
Chao Han, Debabrota Basu, Michael Mangan +2
Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov D…
Optimal Classification under Performative Distribution Shift
Edwige Cyffers, Muni Sreenivas Pydi, Jamal Atif +1
Performative learning addresses the increasingly pervasive situations in which algorithmic decisions may induce changes in the data distribution as a consequence of their public de…