| 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 | 357 | 2013 |
| 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 | 143 | 2011 |
| Concentration inequalities for sampling without replacement R Bardenet, OA Maillard Bernoulli 21 (3), 1361-1385, 2015 | 140 | 2015 |
| Compressed least-squares regression OA Maillard, R Munos Advances in Neural Information Processing Systems, 2009 | 123 | 2009 |
| Latent Bandits. OA Maillard, S Mannor International Conference on Machine Learning, 136-144, 2014 | 76 | 2014 |
| LSTD with random projections M Ghavamzadeh, A Lazaric, OA Maillard, R Munos Advances in Neural Information Processing Systems 23, 721--729, 2010 | 68 | 2010 |
| The non-stationary stochastic multi-armed bandit problem R Allesiardo, R Féraud, OA Maillard International Journal of Data Science and Analytics 3 (4), 267-283, 2017 | 64 | 2017 |
| Robust risk-averse stochastic multi-armed bandits OA Maillard International Conference on Algorithmic Learning Theory, 218-233, 2013 | 55 | 2013 |
| Sub-sampling for multi-armed bandits A Baransi, OA Maillard, S Mannor Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014 | 52 | 2014 |
| Linear regression with random projections O Maillard, R Munos Journal of Machine Learning Research 13 (1), 2735-2772, 2012 | 52 | 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 | 48 | 2014 |
| 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 | 43 | 2010 |
| Variance-aware regret bounds for undiscounted reinforcement learning in mdps MS Talebi, OA Maillard Algorithmic Learning Theory, 770-805, 2018 | 42 | 2018 |
| Selecting the state-representation in reinforcement learning OA Maillard, D Ryabko, R Munos Advances in Neural Information Processing Systems 24, 2011 | 38 | 2011 |
| Online learning in adversarial lipschitz environments OA Maillard, R Munos Joint european conference on machine learning and knowledge discovery in …, 2010 | 37 | 2010 |
| Adaptive Bandits: Towards the best history-dependent strategy OA Maillard, R Munos Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 35* | 2011 |
| 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 | 31 | 2018 |
| Low-rank bandits with latent mixtures A Gopalan, OA Maillard, M Zaki arXiv preprint arXiv:1609.01508, 2016 | 26 | 2016 |
| Optimal regret bounds for selecting the state representation in reinforcement learning OA Maillard, P Nguyen, R Ortner, D Ryabko International Conference on Machine Learning, 543-551, 2013 | 25 | 2013 |
| Selecting near-optimal approximate state representations in reinforcement learning R Ortner, OA Maillard, D Ryabko International Conference on Algorithmic Learning Theory, 140-154, 2014 | 22 | 2014 |
Rémi MunosDeepMindVerifierad e-postadress på inria.fr
