A minimax and asymptotically optimal algorithm for stochastic bandits. - Pierre Ménard; Aurélien Garivier (ALT, 2017)

Fano's inequality for random variables. - Sébastien Gerchinovitz; Pierre Ménard; Gilles Stoltz (CoRR, 2017)

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 Ménard; Rémi Munos; Michal Valko (NeurIPS, 2019)

Gradient Ascent for Active Exploration in Bandit Problems. - Pierre Ménard (CoRR, 2019)

Explore First, Exploit Next: The True Shape of Regret in Bandit Problems. - Aurélien Garivier; Pierre Ménard; Gilles Stoltz (Math. Oper. Res., 2019)

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

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

The Influence of Shape Constraints on the Thresholding Bandit Problem. - James Cheshire; Pierre Ménard; Alexandra Carpentier (COLT, 2020)

Gamification of Pure Exploration for Linear Bandits. - Rémy Degenne; Pierre Ménard; Xuedong Shang; Michal Valko (ICML, 2020)

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. - Anders Jonsson 001; Emilie Kaufmann; Pierre Ménard; Omar Darwiche Domingues; Edouard Leurent; Michal Valko (NeurIPS, 2020)

Regret Bounds for Kernel-Based Reinforcement Learning. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (CoRR, 2020)

Forced-exploration free Strategies for Unimodal Bandits. - Hassan Saber; Pierre Ménard; Odalric-Ambrym Maillard (CoRR, 2020)

Optimal Strategies for Graph-Structured Bandits. - Hassan Saber; Pierre Ménard; Odalric-Ambrym Maillard (CoRR, 2020)

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

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

Adaptive Reward-Free Exploration. - Emilie Kaufmann; Pierre Ménard; Omar Darwiche Domingues; Anders Jonsson 001; Edouard Leurent; Michal Valko (ALT, 2021)

Problem Dependent View on Structured Thresholding Bandit Problems. - James Cheshire; Pierre Ménard; Alexandra Carpentier (ICML, 2021)

Kernel-Based Reinforcement Learning: A Finite-Time Analysis. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (ICML, 2021)

Fast active learning for pure exploration in reinforcement learning. - Pierre Ménard; Omar Darwiche Domingues; Anders Jonsson 001; Emilie Kaufmann; Edouard Leurent; Michal Valko (ICML, 2021)

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

Bandits with many optimal arms. - Rianne de Heide; James Cheshire; Pierre Ménard; Alexandra Carpentier (NeurIPS, 2021)

Learning in two-player zero-sum partially observable Markov games with perfect recall. - Tadashi Kozuno; Pierre Ménard; Rémi Munos; Michal Valko (NeurIPS, 2021)

Indexed Minimum Empirical Divergence for Unimodal Bandits. - Hassan Saber; Pierre Ménard; Odalric-Ambrym Maillard (NeurIPS, 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)

Adaptive Multi-Goal Exploration. - Jean Tarbouriech; Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Michal Valko; Alessandro Lazaric (AISTATS, 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 (ICML, 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 001; Michal Valko; Pierre Ménard (NeurIPS, 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; et al. (CoRR, 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)

Adapting to game trees in zero-sum imperfect information games. - Côme Fiegel; Pierre Ménard; Tadashi Kozuno; Rémi Munos; Vianney Perchet; Michal Valko (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 Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (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; Pierre Ménard (ICML, 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 (NeurIPS, 2023)

Learning Generative Models with Goal-conditioned Reinforcement Learning. - Mariana Vargas Vieyra; Pierre Ménard (CoRR, 2023)

Demonstration-Regularized RL. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (ICLR, 2024)

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

A New Bound on the Cumulant Generating Function of Dirichlet Processes. - Pierre Perrault; Denis Belomestny; Pierre Ménard; Éric Moulines; Alexey Naumov; Daniil Tiapkin; Michal Valko (CoRR, 2024)

Optimal Design for Reward Modeling in RLHF. - Antoine Scheid; Etienne Boursier; Alain Durmus; Michael I. Jordan; Pierre Ménard; Eric Moulines; Michal Valko (CoRR, 2024)

Accelerating Nash Learning from Human Feedback via Mirror Prox. - Daniil Tiapkin; Daniele Calandriello; Denis Belomestny; Eric Moulines; Alexey Naumov; Kashif Rasul; Michal Valko; Pierre Ménard (CoRR, 2025)