Publications

(You can find my publications on my Google Scholar profile)

• Adapting to game trees in zero-sum imperfect information games Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet and Michal Valko, ICML 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 and Pierre Ménard, ICML 2023.

• 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 and Pierre Ménard, NeurIPS 2022.

• Learning Generative Models with Goal-conditioned Reinforcement Learning Mariana Vargas Vieyra and Pierre Ménard, EWRL 2022.

• From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses Daniil Tiapkin, Denis Belomestny, Eric Moulines, Rémi Munos, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko and Pierre Ménard, ICML 2022.

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

• Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall Tadashi Kozuno, Pierre Ménard, Rémi Munos and Michal Valko, NeurIPS 2021.

• Bandits with many optimal arms Rianne de Heide, James Cheshire, Pierre Ménard and Alexandra Carpentier, NeurIPS 2021.

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

• UCB Momentum Q-learning: Correcting the bias without forgetting Pierre Ménard, Omar Darwiche Domingues, Xuedong Shang and Michal Valko, ICML 2021.

• Problem Dependent View on Structured Thresholding Bandit Problems James Cheshire, Pierre Ménard and Alexandra Carpentier, ICML 2021.

• Fast active learning for pure exploration in reinforcement learning Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent and Michal Valko, ICML 2021.

• Regret bounds for kernel-based reinforcement learning Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann and Michal Valko, ICML 2021.

• A kernel-based approach to non-stationary reinforcement learning in metric spaces Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann and Michal Valko, AISTATS 2021.

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

• Adaptive reward-free exploration Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent and Michal Valko, ALT 2021.

• Optimal Strategies for Graph-Structured Bandits Hassan Saber, Pierre Ménard and Odalric-Ambrym Maillard, preprint 2020.

• Planning in Markov decision processes with gap-dependent sample complexity Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent and Michal Valko, NeurIPS 2020.

• The Influence of Shape Constraints on the Thresholding Bandit Problem James Cheshire, Pierre Ménard and Alexandra Carpentier, COLT 2020.

• A single algorithm for both restless and rested rotting bandits Julien Seznec, Pierre Ménard, Alessandro Lazaric and Michal Valko, AISTATS 2020.

• Fixed-confidence guarantees for Bayesian best-arm identification Xuedong Shang, Rianne de Heide, Pierre Ménard, Emilie Kaufmann and Michal Valko, AISTATS 2020.

• Gamification of pure exploration for linear bandits Rémy Degenne, Pierre Ménard, Xuedong Shang and Michal Valko, ICML 2020.

• Thresholding Bandit for Dose-ranging: The Impact of Monotonicity Laurent Rossi, Pierre Ménard and Aurélien Garivier, ICMA 2020.

• Fano’s inequality for random variables Sébastien Gerchinovitz, Pierre Ménard and Gilles Stoltz, Statistical Science 2020.

• Non-asymptotic pure exploration by solving games Rémy Degenne, Wouter Koolen and Pierre Ménard, NeurIPS 2019.

• Planning in entropy-regularized Markov decision processes and games Jean-Bastien Grill, Omar Darwiche Domingues, Pierre Ménard, Rémi Munos and Michal Valko, NeurIPS 2019.

• Gradient Ascent for Active Exploration in Bandit Problems Pierre Ménard, preprint 2019.

• 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 and Gilles Stoltz, JMLR 2022.

• Explore first, exploit next: the true shape of regret in bandit problems Aurélien Garivier, Pierre Ménard and Gilles Stoltz, MOR 2018.

• A minimax and asymptotically optimal algorithm for stochastic bandits Pierre Ménard and Aurélien Garivier, ALT 2017.