| The multi-armed bandit problem with covariates V Perchet, P Rigollet | 160 | 2013 |
| Batched bandit problems V Perchet, P Rigollet, S Chassang, E Snowberg The Annals of Statistics, 660-681, 2016 | 157 | 2016 |
| Bounded regret in stochastic multi-armed bandits S Bubeck, V Perchet, P Rigollet Conference on Learning Theory, 122-134, 2013 | 103 | 2013 |
| Gaussian process optimization with mutual information E Contal, V Perchet, N Vayatis | 94 | 2014 |
| SIC-MMAB: synchronisation involves communication in multiplayer multi-armed bandits E Boursier, V Perchet Advances in Neural Information Processing Systems 32, 2019 | 86 | 2019 |
| Online learning in repeated auctions J Weed, V Perchet, P Rigollet Conference on Learning Theory, 1562-1583, 2016 | 73 | 2016 |
| Stochastic bandit models for delayed conversions C Vernade, O Cappé, V Perchet arXiv preprint arXiv:1706.09186, 2017 | 65 | 2017 |
| Highly-smooth zero-th order online optimization F Bach, V Perchet Conference on Learning Theory, 257-283, 2016 | 64 | 2016 |
| 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 | 57* | 2020 |
| Anytime optimal algorithms in stochastic multi-armed bandits R Degenne, V Perchet International Conference on Machine Learning, 1587-1595, 2016 | 56 | 2016 |
| Approachability, regret and calibration; implications and equivalences V Perchet arXiv preprint arXiv:1301.2663, 2013 | 47* | 2013 |
| Combinatorial semi-bandit with known covariance R Degenne, V Perchet Advances in Neural Information Processing Systems 29, 2016 | 40 | 2016 |
| Fast rates for bandit optimization with upper-confidence frank-wolfe Q Berthet, V Perchet Advances in Neural Information Processing Systems 30, 2017 | 37 | 2017 |
| Set-valued approachability and online learning with partial monitoring S Mannor, V Perchet, G Stoltz The Journal of Machine Learning Research 15 (1), 3247-3295, 2014 | 36* | 2014 |
| Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms. V Perchet Journal of Machine Learning Research 12 (6), 2011 | 36 | 2011 |
| Quantitative analysis of dynamic fault trees based on the coupling of structure functions and Monte Carlo simulation G Merle, JM Roussel, JJ Lesage, V Perchet, N Vayatis Quality and Reliability Engineering International 32 (1), 7-18, 2016 | 35 | 2016 |
| Learning to bid in revenue-maximizing auctions T Nedelec, N El Karoui, V Perchet International Conference on Machine Learning, 4781-4789, 2019 | 34* | 2019 |
| Approachability of convex sets in games with partial monitoring V Perchet Journal of Optimization Theory and Applications 149, 665-677, 2011 | 29 | 2011 |
| Calibration and Internal No-Regret with Random Signals. V Perchet ALT, 68-82, 2009 | 29* | 2009 |
| Statistical efficiency of thompson sampling for combinatorial semi-bandits P Perrault, E Boursier, M Valko, V Perchet Advances in Neural Information Processing Systems 33, 5429-5440, 2020 | 27 | 2020 |
Philippe RigolletMassachusetts Institute of TechnologyAdresse e-mail validée de math.mit.edu
