Pierre Ménard

According to our database, Pierre Ménard authored at least 37 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Demonstration-Regularized RL.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

CoRR, 2023

Local and adaptive mirror descents in extensive-form games.

Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko

CoRR, 2023

Learning Generative Models with Goal-conditioned Reinforcement Learning.

Mariana Vargas Vieyra, Pierre Ménard

CoRR, 2023

Model-free Posterior Sampling via Learning Rate Randomization.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fast Rates for Maximum Entropy Exploration.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 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 Ménard, Mohammad Gheshlaghi Azar, Rémi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvári, Wataru Kumagai, Yutaka Matsuo

Proceedings of the International Conference on Machine Learning, 2023

Adapting to game trees in zero-sum imperfect information games.

Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko

Proceedings of the International Conference on Machine Learning, 2023

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

J. Mach. Learn. Res., 2022

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal.

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

CoRR, 2022

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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 Ménard

Proceedings of the International Conference on Machine Learning, 2022

Adaptive Multi-Goal Exploration.

Jean Tarbouriech, Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Michal Valko, Alessandro Lazaric

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall.

Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko

CoRR, 2021

Indexed Minimum Empirical Divergence for Unimodal Bandits.

Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning in two-player zero-sum partially observable Markov games with perfect recall.

Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bandits with many optimal arms.

Rianne de Heide, James Cheshire, Pierre Ménard, Alexandra Carpentier

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

UCB Momentum Q-learning: Correcting the bias without forgetting.

Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang, Michal Valko

Proceedings of the 38th International Conference on Machine Learning, 2021

Fast active learning for pure exploration in reinforcement learning.

Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko

Proceedings of the 38th International Conference on Machine Learning, 2021

Kernel-Based Reinforcement Learning: A Finite-Time Analysis.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

Proceedings of the 38th International Conference on Machine Learning, 2021

Problem Dependent View on Structured Thresholding Bandit Problems.

James Cheshire, Pierre Ménard, Alexandra Carpentier

Proceedings of the 38th International Conference on Machine Learning, 2021

Adaptive Reward-Free Exploration.

Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko

Proceedings of the Algorithmic Learning Theory, 2021

Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited.

Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko

Proceedings of the Algorithmic Learning Theory, 2021

A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Optimal Strategies for Graph-Structured Bandits.

Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard

CoRR, 2020

Forced-exploration free Strategies for Unimodal Bandits.

Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard

CoRR, 2020

Regret Bounds for Kernel-Based Reinforcement Learning.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

CoRR, 2020

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity.

Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Gamification of Pure Exploration for Linear Bandits.

Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko

Proceedings of the 37th International Conference on Machine Learning, 2020

The Influence of Shape Constraints on the Thresholding Bandit Problem.

James Cheshire, Pierre Ménard, Alexandra Carpentier

Proceedings of the Conference on Learning Theory, 2020

Fixed-confidence guarantees for Bayesian best-arm identification.

Xuedong Shang, Rianne de Heide, Pierre Ménard, Emilie Kaufmann, Michal Valko

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A single algorithm for both restless and rested rotting bandits.

Julien Seznec, Pierre Ménard, Alessandro Lazaric, Michal Valko

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Explore First, Exploit Next: The True Shape of Regret in Bandit Problems.

Aurélien Garivier, Pierre Ménard, Gilles Stoltz

Math. Oper. Res., 2019

Gradient Ascent for Active Exploration in Bandit Problems.

Pierre Ménard

CoRR, 2019

Planning in entropy-regularized Markov decision processes and games.

Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos, Michal Valko

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Non-Asymptotic Pure Exploration by Solving Games.

Rémy Degenne, Wouter M. Koolen, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Fano's inequality for random variables.

Sébastien Gerchinovitz, Pierre Ménard, Gilles Stoltz

CoRR, 2017

A minimax and asymptotically optimal algorithm for stochastic bandits.

Pierre Ménard, Aurélien Garivier

Proceedings of the International Conference on Algorithmic Learning Theory, 2017