Pierre Ménard

68 papers A* 21A 4B 3Journal 40

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments. CoRR Romain Froger, Pierre Andrews, Matteo Bettini, Amar Budhiraja, Ricardo Silveira Cabral, Virginie Do, Emilien Garreau, Jean-Baptiste Gaya, Hugo Laurençon, Maxime Lecanu, Kunal Malkan, Dheeraj Mekala, Pierre Ménard, Gerard Moreno-Torres Bertran, Ulyana Piterbarg, Mikhail Plekhanov, Mathieu Rita, Andrey Rusakov, Vladislav Vorotilov, Mengjue Wang, Ian Yu, Amine Benhalloum, Grégoire Mialon, Thomas Scialom
2025	J	`jnl`	ARE: Scaling Up Agent Environments and Evaluations. CoRR Pierre Andrews, Amine Benhalloum, Gerard Moreno-Torres Bertran, Matteo Bettini, Amar Budhiraja, Ricardo Silveira Cabral, Virginie Do, Romain Froger, Emilien Garreau, Jean-Baptiste Gaya, Hugo Laurençon, Maxime Lecanu, Kunal Malkan, Dheeraj Mekala, Pierre Ménard, Grégoire Mialon, Ulyana Piterbarg, Mikhail Plekhanov, Mathieu Rita, Andrey Rusakov, Thomas Scialom, Vladislav Vorotilov, Mengjue Wang, Ian Yu
2025	J	`jnl`	Accelerating Nash Learning from Human Feedback via Mirror Prox. CoRR Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines, Alexey Naumov, Kashif Rasul, Michal Valko, Pierre Ménard
2025	A*	`conf`	The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback. ICML Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Michal Valko, Vianney Perchet
2024	J	`jnl`	A New Bound on the Cumulant Generating Function of Dirichlet Processes. CoRR Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko
2024	A*	`conf`	Demonstration-Regularized RL. ICLR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2024	A*	`conf`	Local and Adaptive Mirror Descents in Extensive-Form Games. NeurIPS Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2024	J	`jnl`	Optimal Design for Reward Modeling in RLHF. CoRR Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan, Pierre Ménard, Eric Moulines, Michal Valko
2023	A*	`conf`	Adapting to game trees in zero-sum imperfect information games. ICML Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2023	J	`jnl`	Demonstration-Regularized RL. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	A*	`conf`	Fast Rates for Maximum Entropy Exploration. ICML Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Fast Rates for Maximum Entropy Exploration. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Learning Generative Models with Goal-conditioned Reinforcement Learning. CoRR Mariana Vargas Vieyra, Pierre Ménard
2023	J	`jnl`	Local and adaptive mirror descents in extensive-form games. CoRR Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2023	A*	`conf`	Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Model-free Posterior Sampling via Learning Rate Randomization. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	A*	`conf`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. ICML 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
2023	J	`jnl`	Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. CoRR 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
2022	J	`jnl`	Adapting to game trees in zero-sum imperfect information games. CoRR Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko
2022	A	`conf`	Adaptive Multi-Goal Exploration. AISTATS Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2022	A*	`conf`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	J	`jnl`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	J	`jnl`	KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. CoRR 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
2022	J	`jnl`	KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints. J. Mach. Learn. Res. Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz
2022	A*	`conf`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	J	`jnl`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2021	A	`conf`	A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. AISTATS Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
2021	J	`jnl`	Adaptive Multi-Goal Exploration. CoRR Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric
2021	B	`conf`	Adaptive Reward-Free Exploration. ALT Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko
2021	A*	`conf`	Bandits with many optimal arms. NeurIPS Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier
2021	J	`jnl`	Bandits with many optimal arms. CoRR Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier
2021	B	`conf`	Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. ALT Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko
2021	A*	`conf`	Fast active learning for pure exploration in reinforcement learning. ICML Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
2021	A*	`conf`	Indexed Minimum Empirical Divergence for Unimodal Bandits. NeurIPS Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
2021	J	`jnl`	Indexed Minimum Empirical Divergence for Unimodal Bandits. CoRR Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
2021	A*	`conf`	Kernel-Based Reinforcement Learning: A Finite-Time Analysis. ICML Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
2021	A*	`conf`	Learning in two-player zero-sum partially observable Markov games with perfect recall. NeurIPS Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
2021	J	`jnl`	Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall. CoRR Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
2021	A*	`conf`	Problem Dependent View on Structured Thresholding Bandit Problems. ICML James Cheshire, Pierre Ménard, Alexandra Carpentier
2021	J	`jnl`	Problem Dependent View on Structured Thresholding Bandit Problems. CoRR James Cheshire, Pierre Ménard, Alexandra Carpentier
2021	A*	`conf`	UCB Momentum Q-learning: Correcting the bias without forgetting. ICML Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
2021	J	`jnl`	UCB Momentum Q-learning: Correcting the bias without forgetting. CoRR Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko
2020	J	`jnl`	A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. CoRR Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
2020	A	`conf`	A single algorithm for both restless and rested rotting bandits. AISTATS Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko
2020	J	`jnl`	Adaptive Reward-Free Exploration. CoRR Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko
2020	J	`jnl`	Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. CoRR Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko
2020	J	`jnl`	Fast active learning for pure exploration in reinforcement learning. CoRR Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko
2020	A	`conf`	Fixed-confidence guarantees for Bayesian best-arm identification. AISTATS Xuedong Shang, Rianne de Heide, Pierre Ménard, Emilie Kaufmann, Michal Valko
2020	J	`jnl`	Forced-exploration free Strategies for Unimodal Bandits. CoRR Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
2020	A*	`conf`	Gamification of Pure Exploration for Linear Bandits. ICML Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
2020	J	`jnl`	Gamification of Pure Exploration for Linear Bandits. CoRR Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko
2020	J	`jnl`	Optimal Strategies for Graph-Structured Bandits. CoRR Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
2020	A*	`conf`	Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. NeurIPS Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko
2020	J	`jnl`	Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. CoRR Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko
2020	J	`jnl`	Regret Bounds for Kernel-Based Reinforcement Learning. CoRR Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
2020	A*	`conf`	The Influence of Shape Constraints on the Thresholding Bandit Problem. COLT James Cheshire, Pierre Ménard, Alexandra Carpentier
2020	J	`jnl`	The Influence of Shape Constraints on the Thresholding Bandit Problem. CoRR James Cheshire, Pierre Ménard, Alexandra Carpentier
2019	J	`jnl`	Explore First, Exploit Next: The True Shape of Regret in Bandit Problems. Math. Oper. Res. Aurélien Garivier, Pierre Ménard, Gilles Stoltz
2019	J	`jnl`	Fixed-Confidence Guarantees for Bayesian Best-Arm Identification. CoRR Xuedong Shang, Rianne de Heide, Emilie Kaufmann, Pierre Ménard, Michal Valko
2019	J	`jnl`	Gradient Ascent for Active Exploration in Bandit Problems. CoRR Pierre Ménard
2019	A*	`conf`	Non-Asymptotic Pure Exploration by Solving Games. NeurIPS Rémy Degenne, Wouter M. Koolen, Pierre Ménard
2019	J	`jnl`	Non-Asymptotic Pure Exploration by Solving Games. CoRR Rémy Degenne, Wouter M. Koolen, Pierre Ménard
2019	A*	`conf`	Planning in entropy-regularized Markov decision processes and games. NeurIPS Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko
2018	J	`jnl`	KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints. CoRR Aurélien Garivier, Hédi Hadiji, Pierre Ménard, Gilles Stoltz
2017	B	`conf`	A minimax and asymptotically optimal algorithm for stochastic bandits. ALT Pierre Ménard, Aurélien Garivier
2017	J	`jnl`	A minimax and asymptotically optimal algorithm for stochastic bandits. CoRR Pierre Ménard, Aurélien Garivier
2017	J	`jnl`	Fano's inequality for random variables. CoRR Sébastien Gerchinovitz, Pierre Ménard, Gilles Stoltz
2016	J	`jnl`	Explore First, Exploit Next: The True Shape of Regret in Bandit Problems. CoRR Aurélien Garivier, Pierre Ménard, Gilles Stoltz