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Pierre Ménard
Pierre Ménard
OvGU Magdeburg
Verified email at inria.fr - Homepage
Title
Cited by
Cited by
Year
Explore first, exploit next: The true shape of regret in bandit problems
A Garivier, P Ménard, G Stoltz
Mathematics of Operations Research 44 (2), 377-399, 2019
1962019
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited
O Darwiche Domingues, P Ménard, E Kaufmann, M Valko
arXiv e-prints, arXiv: 2010.03531, 2020
110*2020
Fast active learning for pure exploration in reinforcement learning
P Ménard, OD Domingues, A Jonsson, E Kaufmann, E Leurent, M Valko
International Conference on Machine Learning, 7599-7608, 2021
972021
Adaptive reward-free exploration
E Kaufmann, P Ménard, OD Domingues, A Jonsson, E Leurent, M Valko
Algorithmic Learning Theory, 865-891, 2021
892021
Non-asymptotic pure exploration by solving games
R Degenne, WM Koolen, P Ménard
Advances in Neural Information Processing Systems 32, 2019
872019
Gamification of pure exploration for linear bandits
R Degenne, P Ménard, X Shang, M Valko
International Conference on Machine Learning, 2432-2442, 2020
812020
Fixed-confidence guarantees for bayesian best-arm identification
X Shang, R Heide, P Menard, E Kaufmann, M Valko
International Conference on Artificial Intelligence and Statistics, 1823-1832, 2020
652020
A minimax and asymptotically optimal algorithm for stochastic bandits
P Ménard, A Garivier
International Conference on Algorithmic Learning Theory, 223-237, 2017
562017
Kernel-based reinforcement learning: A finite-time analysis
OD Domingues, P Ménard, M Pirotta, E Kaufmann, M Valko
International Conference on Machine Learning, 2783-2792, 2021
48*2021
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints
A Garivier, H Hadiji, P Menard, G Stoltz
Journal of Machine Learning Research 23 (179), 1-66, 2022
432022
Ucb momentum q-learning: Correcting the bias without forgetting
P Ménard, OD Domingues, X Shang, M Valko
International Conference on Machine Learning, 7609-7618, 2021
412021
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
O Darwiche Domingues, P Ménard, M Pirotta, E Kaufmann, M Valko
arXiv e-prints, arXiv: 2007.05078, 2020
38*2020
Fano’s inequality for random variables
S Gerchinovitz, P Ménard, G Stoltz
362020
Model-free learning for two-player zero-sum partially observable markov games with perfect recall
T Kozuno, P Ménard, R Munos, M Valko
arXiv preprint arXiv:2106.06279, 2021
34*2021
A single algorithm for both restless and rested rotting bandits
J Seznec, P Menard, A Lazaric, M Valko
International Conference on Artificial Intelligence and Statistics, 3784-3794, 2020
332020
Planning in markov decision processes with gap-dependent sample complexity
A Jonsson, E Kaufmann, P Ménard, O Darwiche Domingues, E Leurent, ...
Advances in Neural Information Processing Systems 33, 1253-1263, 2020
292020
Thresholding bandit for dose-ranging: The impact of monotonicity
A Garivier, P Ménard, L Rossi, P Menard
arXiv preprint arXiv:1711.04454, 2017
292017
Bandits with many optimal arms
R De Heide, J Cheshire, P Ménard, A Carpentier
Advances in Neural Information Processing Systems 34, 22457-22469, 2021
192021
Planning in entropy-regularized Markov decision processes and games
JB Grill, O Darwiche Domingues, P Ménard, R Munos, M Valko
Advances in Neural Information Processing Systems 32, 2019
192019
Gradient ascent for active exploration in bandit problems
P Ménard
arXiv preprint arXiv:1905.08165, 2019
182019
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