Menard, Pierre

29 publications

ICML 2025 The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback Côme Fiegel, Pierre Menard, Tadashi Kozuno, Michal Valko, Vianney Perchet

ICLR 2024 Demonstration-Regularized RL Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Menard

NeurIPS 2024 Local and Adaptive Mirror Descents in Extensive-Form Games Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko

ICML 2023 Adapting to Game Trees in Zero-Sum Imperfect Information Games Côme Fiegel, Pierre Menard, Tadashi Kozuno, Remi Munos, Vianney Perchet, Michal Valko

ICML 2023 Fast Rates for Maximum Entropy Exploration Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Remi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Menard

NeurIPS 2023 Model-Free Posterior Sampling via Learning Rate Randomization Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Remi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

ICML 2023 Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Menard, Mohammad Gheshlaghi Azar, Remi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvari, Wataru Kumagai, Yutaka Matsuo

AISTATS 2022 Adaptive Multi-Goal Exploration Jean Tarbouriech, Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Michal Valko, Alessandro Lazaric

ICML 2022 From Dirichlet to Rubin: Optimistic Exploration in RL Without Bonuses Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Menard

JMLR 2022 KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz

NeurIPS 2022 Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Remi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard

AISTATS 2021 A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

ALT 2021 Adaptive Reward-Free Exploration Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko

NeurIPS 2021 Bandits with Many Optimal Arms Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier

ALT 2021 Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko

ICML 2021 Fast Active Learning for Pure Exploration in Reinforcement Learning Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko

NeurIPS 2021 Indexed Minimum Empirical Divergence for Unimodal Bandits Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard

ICML 2021 Kernel-Based Reinforcement Learning: A Finite-Time Analysis Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

NeurIPS 2021 Learning in Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall Tadashi Kozuno, Pierre Ménard, Remi Munos, Michal Valko

ICML 2021 Problem Dependent View on Structured Thresholding Bandit Problems James Cheshire, Pierre Menard, Alexandra Carpentier

ICML 2021 UCB Momentum Q-Learning: Correcting the Bias Without Forgetting Pierre Menard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko

AISTATS 2020 A Single Algorithm for Both Restless and Rested Rotting Bandits Julien Seznec, Pierre Menard, Alessandro Lazaric, Michal Valko

AISTATS 2020 Fixed-Confidence Guarantees for Bayesian Best-Arm Identification Xuedong Shang, Rianne Heide, Pierre Menard, Emilie Kaufmann, Michal Valko

ICML 2020 Gamification of Pure Exploration for Linear Bandits Rémy Degenne, Pierre Menard, Xuedong Shang, Michal Valko

NeurIPS 2020 Planning in Markov Decision Processes with Gap-Dependent Sample Complexity Anders Jonsson, Emilie Kaufmann, Pierre Menard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko

COLT 2020 The Influence of Shape Constraints on the Thresholding Bandit Problem James Cheshire, Pierre Menard, Alexandra Carpentier

NeurIPS 2019 Non-Asymptotic Pure Exploration by Solving Games Rémy Degenne, Wouter M. Koolen, Pierre Ménard

NeurIPS 2019 Planning in Entropy-Regularized Markov Decision Processes and Games Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Menard, Remi Munos, Michal Valko

ALT 2017 A Minimax and Asymptotically Optimal Algorithm for Stochastic Bandits Pierre Ménard, Aurélien Garivier