687 citations
- Centre de Recherche en Informatique, Signal et Automatique de LilleFR203 papers
- Université de LilleFR180 papers
- Centre National de la Recherche ScientifiqueFR92 papers
- École Centrale de LilleFR73 papers
- Centre Inria de l'Université de LilleFR30 papers
- Institut national de recherche en sciences et technologies du numériqueFR25 papers
- IMT Nord EuropeFR16 papers
- Université d'ArtoisFR16 papers
- Université Paris-SaclayFR16 papers
- Institut d'Astrophysique SpatialeFR15 papers
- Institut de Recherche en Astrophysique et PlanétologieFR14 papers
- Université Paris 1 Panthéon-SorbonneFR14 papers
8 papers · 2 filters
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge
Reda Ouhamma, Odalric Maillard, Vianney Perchet
We consider the problem of online linear regression in the stochastic setting. We derive high probability regret bounds for online ridge regression and the forward algorithm. This…
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
Reda Ouhamma, Rémy Degenne, Pierre Gaillard +1
In the fixed budget thresholding bandit problem, an algorithm sequentially allocates a budgeted number of samples to different distributions. It then predicts whether the mean of e…
Nonparametric estimation of continuous DPPs with kernel methods
Michaël Fanuel, Rémi Bardenet
Determinantal Point Process (DPPs) are statistical models for repulsive point patterns. Both sampling and inference are tractable for DPPs, a rare feature among models with negativ…
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
Nathan Grinsztajn, Johan Ferret, Olivier Pietquin +2
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL). From theoretical considerations, we show…
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
Mathieu Seurin, Florian Strub, Philippe Preux +1
Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize. Intrinsic motivation guidances have thus been developed toward alle…
Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks
Joachim Schreurs, Hannes De Meulemeester, Michaël Fanuel +2
Commonly, machine learning models minimize an empirical expectation. As a result, the trained models typically perform well for the majority of the data but the performance may det…