| Thompson sampling: An asymptotically optimal finite-time analysis E Kaufmann, N Korda, R Munos International conference on algorithmic learning theory, 199-213, 2012 | 528 | 2012 |
| On Bayesian upper confidence bounds for bandit problems E Kaufmann, O Cappé, A Garivier Artificial intelligence and statistics, 592-600, 2012 | 314 | 2012 |
| On the complexity of best-arm identification in multi-armed bandit models E Kaufmann, O Cappé, A Garivier The Journal of Machine Learning Research 17 (1), 1-42, 2016 | 301 | 2016 |
| Optimal best arm identification with fixed confidence A Garivier, E Kaufmann Conference on Learning Theory, 998-1027, 2016 | 125 | 2016 |
| Thompson sampling for 1-dimensional exponential family bandits N Korda, E Kaufmann, R Munos arXiv preprint arXiv:1307.3400, 2013 | 124 | 2013 |
| Information complexity in bandit subset selection E Kaufmann, S Kalyanakrishnan Conference on Learning Theory, 228-251, 2013 | 122 | 2013 |
| Multi-player bandits revisited L Besson, E Kaufmann Algorithmic Learning Theory, 56-92, 2018 | 63 | 2018 |
| On explore-then-commit strategies A Garivier, T Lattimore, E Kaufmann Advances in Neural Information Processing Systems 29, 784-792, 2016 | 49 | 2016 |
| Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings R Bonnefoi, L Besson, C Moy, E Kaufmann, J Palicot International Conference on Cognitive Radio Oriented Wireless Networks, 173-185, 2017 | 47 | 2017 |
| On Bayesian index policies for sequential resource allocation E Kaufmann arXiv preprint arXiv:1601.01190, 2016 | 45 | 2016 |
| On the complexity of A/B testing E Kaufmann, O Cappé, A Garivier Conference on Learning Theory, 461-481, 2014 | 41 | 2014 |
| Mixture martingales revisited with applications to sequential tests and confidence intervals E Kaufmann, W Koolen arXiv preprint arXiv:1811.11419, 2018 | 38 | 2018 |
| What doubling tricks can and can't do for multi-armed bandits L Besson, E Kaufmann arXiv preprint arXiv:1803.06971, 2018 | 36 | 2018 |
| Maximin action identification: A new bandit framework for games A Garivier, E Kaufmann, WM Koolen Conference on Learning Theory, 1028-1050, 2016 | 33 | 2016 |
| Monte-Carlo tree search by best arm identification E Kaufmann, W Koolen arXiv preprint arXiv:1706.02986, 2017 | 32 | 2017 |
| A spectral algorithm with additive clustering for the recovery of overlapping communities in networks E Kaufmann, T Bonald, M Lelarge International Conference on Algorithmic Learning Theory, 355-370, 2016 | 30* | 2016 |
| Analyse de stratégies bayésiennes et fréquentistes pour l'allocation séquentielle de ressources E Kaufmann Paris, ENST, 2014 | 30 | 2014 |
| Machine learning applications in drug development C Réda, E Kaufmann, A Delahaye-Duriez Computational and structural biotechnology journal 18, 241-252, 2020 | 29 | 2020 |
| A practical algorithm for multiplayer bandits when arm means vary among players E Boursier, E Kaufmann, A Mehrabian, V Perchet | 22* | 2020 |
| Corrupt bandits for preserving local privacy P Gajane, T Urvoy, E Kaufmann Algorithmic Learning Theory, 387-412, 2018 | 21 | 2018 |
Aurélien GarivierEcole Normale Supérieure de LyonAdresse e-mail validée de ens-lyon.fr
