Combes, Richard

12 publications

NeurIPS 2025 Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms William Réveillard, Richard Combes

NeurIPS 2024 Thompson Sampling for Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox Raymond Zhang, Richard Combes

AISTATS 2023 Contextual Linear Bandits Under Noisy Features: Towards Bayesian Oracles Jung-Hun Kim, Se-Young Yun, Minchan Jeong, Junhyun Nam, Jinwoo Shin, Richard Combes

COLT 2022 Towards Optimal Algorithms for Multi-Player Bandits Without Collision Sensing Information Wei Huang, Richard Combes, Cindy Trinh

ALT 2021 Asymptotically Optimal Strategies for Combinatorial Semi-Bandits in Polynomial Time Thibaut Cuvelier, Richard Combes, Eric Gourdin

NeurIPS 2021 On the Suboptimality of Thompson Sampling in High Dimensions Raymond Zhang, Richard Combes

ALT 2020 Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes

NeurIPS 2017 A Minimax Optimal Algorithm for Crowdsourcing Thomas Bonald, Richard Combes

NeurIPS 2017 Minimal Exploration in Structured Stochastic Bandits Richard Combes, Stefan Magureanu, Alexandre Proutiere

NeurIPS 2015 Combinatorial Bandits Revisited Richard Combes, Mohammad Sadegh Talebi Mazraeh Shahi, Alexandre Proutiere, Marc Lelarge

COLT 2014 Lipschitz Bandits: Regret Lower Bound and Optimal Algorithms Stefan Magureanu, Richard Combes, Alexandre Proutière

ICML 2014 Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms Richard Combes, Alexandre Proutiere