| Kullback-Leibler upper confidence bounds for optimal sequential allocation O Cappé, A Garivier, OA Maillard, R Munos, G Stoltz The Annals of Statistics, 1516-1541, 2013 | 411 | 2013 |
| Concentration inequalities for sampling without replacement R Bardenet, OA Maillard | 176 | 2015 |
| CATS, a low pressure multiwire proportionnal chamber for secondary beam tracking at GANIL S Ottini-Hustache, C Mazur, F Auger, A Musumarra, N Alamanos, ... Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 1999 | 163 | 1999 |
| A finite-time analysis of multi-armed bandits problems with kullback-leibler divergences OA Maillard, R Munos, G Stoltz Proceedings of the 24th annual Conference On Learning Theory, 497-514, 2011 | 158 | 2011 |
| Compressed least-squares regression OA Maillard, R Munos Advances in Neural Information Processing Systems, 2009 | 135 | 2009 |
| Latent Bandits. OA Maillard, S Mannor International Conference on Machine Learning, 136-144, 2014 | 98 | 2014 |
| The non-stationary stochastic multi-armed bandit problem R Allesiardo, R Féraud, OA Maillard International Journal of Data Science and Analytics 3, 267-283, 2017 | 75 | 2017 |
| Robust risk-averse stochastic multi-armed bandits OA Maillard Algorithmic Learning Theory: 24th International Conference, ALT 2013 …, 2013 | 74 | 2013 |
| LSTD with random projections M Ghavamzadeh, A Lazaric, OA Maillard, R Munos Advances in Neural Information Processing Systems 23, 721--729, 2010 | 74 | 2010 |
| Variance-aware regret bounds for undiscounted reinforcement learning in mdps MS Talebi, OA Maillard Algorithmic Learning Theory, 770-805, 2018 | 67 | 2018 |
| Sub-sampling for multi-armed bandits A Baransi, OA Maillard, S Mannor Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 60 | 2014 |
| Linear regression with random projections O Maillard, R Munos Journal of Machine Learning Research 13 (1), 2735-2772, 2012 | 60 | 2012 |
| How hard is my MDP?" The distribution-norm to the rescue" OA Maillard, TA Mann, S Mannor Advances in Neural Information Processing Systems 27, 2014 | 57 | 2014 |
| PICOSEC: Charged particle timing at sub-25 picosecond precision with a Micromegas based detector J Bortfeldt, F Brunbauer, C David, D Desforge, G Fanourakis, J Franchi, ... Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2018 | 53 | 2018 |
| Online learning in adversarial lipschitz environments OA Maillard, R Munos Joint european conference on machine learning and knowledge discovery in …, 2010 | 46 | 2010 |
| Selecting the state-representation in reinforcement learning OA Maillard, D Ryabko, R Munos Advances in Neural Information Processing Systems 24, 2011 | 45 | 2011 |
| Finite-sample analysis of Bellman residual minimization OA Maillard, R Munos, A Lazaric, M Ghavamzadeh Proceedings of 2nd Asian Conference on Machine Learning, 299-314, 2010 | 45 | 2010 |
| Streaming kernel regression with provably adaptive mean, variance, and regularization A Durand, OA Maillard, J Pineau The Journal of Machine Learning Research 19 (1), 650-683, 2018 | 43 | 2018 |
| Adaptive Bandits: Towards the best history-dependent strategy OA Maillard, R Munos Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 41* | 2011 |
| Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds OA Maillard Algorithmic Learning Theory, 610-632, 2019 | 34 | 2019 |
Rémi MunosDeepMindVerified email at inria.fr
