Pierre Ménard

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 61

Top Topics

Bandit Problems

Markov Decision Process

Reinforcement Learning

Gradient Ascent

Top Venues

Publications

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. ICLR (2024)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. CoRR (2023)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Local and adaptive mirror descents in extensive-form games. CoRR (2023)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Adapting to game trees in zero-sum imperfect information games. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. CoRR (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Model-free Posterior Sampling via Learning Rate Randomization. CoRR (2023)
Mariana Vargas Vieyra, Pierre Ménard
Learning Generative Models with Goal-conditioned Reinforcement Learning. CoRR (2023)
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS (2023)
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Adaptive Multi-Goal Exploration. AISTATS (2022)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS (2022)
Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
Adapting to game trees in zero-sum imperfect information games. CoRR (2022)
Tadashi Kozuno, Wenhao Yang, Nino Vieillard, Toshinori Kitamura, Yunhao Tang, Jincheng Mei, Pierre Ménard, Mohammad Gheshlaghi Azar, Michal Valko, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR (2022)
Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz
KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints. J. Mach. Learn. Res. 23 (2022)
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML (2022)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. AISTATS (2021)
Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
UCB Momentum Q-learning: Correcting the bias without forgetting. CoRR (2021)
Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier
Bandits with many optimal arms. CoRR (2021)
Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. CoRR (2021)
Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
Adaptive Multi-Goal Exploration. CoRR (2021)
Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
UCB Momentum Q-learning: Correcting the bias without forgetting. ICML (2021)
Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. ALT (2021)
Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko
Adaptive Reward-Free Exploration. ALT (2021)
Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
Fast active learning for pure exploration in reinforcement learning. ICML (2021)
James Cheshire, Pierre Ménard, Alexandra Carpentier
Problem Dependent View on Structured Thresholding Bandit Problems. CoRR (2021)
Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier
Bandits with many optimal arms. NeurIPS (2021)
Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
Learning in two-player zero-sum partially observable Markov games with perfect recall. NeurIPS (2021)
James Cheshire, Pierre Ménard, Alexandra Carpentier
Problem Dependent View on Structured Thresholding Bandit Problems. ICML (2021)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
Kernel-Based Reinforcement Learning: A Finite-Time Analysis. ICML (2021)
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
Indexed Minimum Empirical Divergence for Unimodal Bandits. NeurIPS (2021)
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
Indexed Minimum Empirical Divergence for Unimodal Bandits. CoRR (2021)
James Cheshire, Pierre Ménard, Alexandra Carpentier
The Influence of Shape Constraints on the Thresholding Bandit Problem. COLT (2020)
Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
Gamification of Pure Exploration for Linear Bandits. ICML (2020)
James Cheshire, Pierre Ménard, Alexandra Carpentier
The Influence of Shape Constraints on the Thresholding Bandit Problem. CoRR (2020)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. CoRR (2020)
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
Forced-exploration free Strategies for Unimodal Bandits. CoRR (2020)
Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. NeurIPS (2020)
Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko
A single algorithm for both restless and rested rotting bandits. AISTATS (2020)
Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. CoRR (2020)
Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
Gamification of Pure Exploration for Linear Bandits. CoRR (2020)
Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
Fast active learning for pure exploration in reinforcement learning. CoRR (2020)
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
Optimal Strategies for Graph-Structured Bandits. CoRR (2020)
Xuedong Shang, Rianne de Heide, Pierre Ménard, Emilie Kaufmann, Michal Valko
Fixed-confidence guarantees for Bayesian best-arm identification. AISTATS (2020)
Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. CoRR (2020)
Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko
Adaptive Reward-Free Exploration. CoRR (2020)
Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
Regret Bounds for Kernel-Based Reinforcement Learning. CoRR (2020)
Pierre Ménard
Gradient Ascent for Active Exploration in Bandit Problems. CoRR (2019)
Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko
Planning in entropy-regularized Markov decision processes and games. NeurIPS (2019)
Rémy Degenne, Wouter M. Koolen, Pierre Ménard
Non-Asymptotic Pure Exploration by Solving Games. NeurIPS (2019)
Xuedong Shang, Rianne de Heide, Emilie Kaufmann, Pierre Ménard, Michal Valko
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification. CoRR (2019)
Aurélien Garivier, Pierre Ménard, Gilles Stoltz
Explore First, Exploit Next: The True Shape of Regret in Bandit Problems. Math. Oper. Res. 44 (2) (2019)
Rémy Degenne, Wouter M. Koolen, Pierre Ménard
Non-Asymptotic Pure Exploration by Solving Games. CoRR (2019)
Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints. CoRR (2018)
Sébastien Gerchinovitz, Pierre Ménard, Gilles Stoltz
Fano's inequality for random variables. CoRR (2017)
Pierre Ménard, Aurélien Garivier
A minimax and asymptotically optimal algorithm for stochastic bandits. ALT (2017)
Pierre Ménard, Aurélien Garivier
A minimax and asymptotically optimal algorithm for stochastic bandits. CoRR (2017)
Aurélien Garivier, Pierre Ménard, Gilles Stoltz
Explore First, Exploit Next: The True Shape of Regret in Bandit Problems. CoRR (2016)