| Thompson sampling: An asymptotically optimal finite-time analysis E Kaufmann, N Korda, R Munos International conference on algorithmic learning theory, 199-213, 2012 | 745 | 2012 |
| On the complexity of best arm identification in multi-armed bandit models E Kaufmann, O Cappé, A Garivier Journal of Machine Learning Research 17, 1-42, 2016 | 554 | 2016 |
| On Bayesian upper confidence bounds for bandit problems E Kaufmann, O Cappé, A Garivier Artificial intelligence and statistics, 592-600, 2012 | 431 | 2012 |
| Optimal best arm identification with fixed confidence A Garivier, E Kaufmann Conference on Learning Theory, 998-1027, 2016 | 332 | 2016 |
| Information complexity in bandit subset selection E Kaufmann, S Kalyanakrishnan Conference on Learning Theory, 228-251, 2013 | 200 | 2013 |
| Thompson sampling for 1-dimensional exponential family bandits N Korda, E Kaufmann, R Munos Advances in neural information processing systems 26, 2013 | 180 | 2013 |
| Machine learning applications in drug development C Réda, E Kaufmann, A Delahaye-Duriez Computational and structural biotechnology journal 18, 241-252, 2020 | 179 | 2020 |
| Multi-player bandits revisited L Besson, E Kaufmann Algorithmic Learning Theory, 56-92, 2018 | 111 | 2018 |
| On explore-then-commit strategies A Garivier, T Lattimore, E Kaufmann Advances in Neural Information Processing Systems 29, 2016 | 108 | 2016 |
| Mixture martingales revisited with applications to sequential tests and confidence intervals E Kaufmann, WM Koolen The Journal of Machine Learning Research 22 (1), 11140-11183, 2021 | 101 | 2021 |
| 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 | 101 | 2017 |
| Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited OD Domingues, P Ménard, E Kaufmann, M Valko Algorithmic Learning Theory, 578-598, 2021 | 100 | 2021 |
| What doubling tricks can and can't do for multi-armed bandits L Besson, E Kaufmann arXiv preprint arXiv:1803.06971, 2018 | 93 | 2018 |
| Adaptive reward-free exploration E Kaufmann, P Ménard, OD Domingues, A Jonsson, E Leurent, M Valko Algorithmic Learning Theory, 865-891, 2021 | 76 | 2021 |
| On the complexity of A/B testing E Kaufmann, O Cappé, A Garivier Conference on Learning Theory, 461-481, 2014 | 69 | 2014 |
| A practical algorithm for multiplayer bandits when arm means vary among players A Mehrabian, E Boursier, E Kaufmann, V Perchet International Conference on Artificial Intelligence and Statistics, 1211-1221, 2020 | 68 | 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 | 61 | 2021 |
| 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 | 59 | 2020 |
| A spectral algorithm with additive clustering for the recovery of overlapping communities in networks E Kaufmann, T Bonald, M Lelarge Theoretical Computer Science 742, 3-26, 2018 | 51* | 2018 |
| On multi-armed bandit designs for dose-finding clinical trials M Aziz, E Kaufmann, MK Riviere The Journal of Machine Learning Research 22 (1), 686-723, 2021 | 50 | 2021 |
Aurélien GarivierEcole Normale Supérieure de LyonПотвърден имейл адрес: ens-lyon.fr
