| Batched bandit problems V Perchet, P Rigollet, S Chassang, E Snowberg | 269 | 2016 |
| The multi-armed bandit problem with covariates V Perchet, P Rigollet | 210 | 2013 |
| Gaussian process optimization with mutual information E Contal, V Perchet, N Vayatis | 122 | 2014 |
| Bounded regret in stochastic multi-armed bandits S Bubeck, V Perchet, P Rigollet Conference on Learning Theory, 122-134, 2013 | 118 | 2013 |
| SIC-MMAB: Synchronisation involves communication in multiplayer multi-armed bandits E Boursier, V Perchet Advances in Neural Information Processing Systems 32, 2019 | 114 | 2019 |
| Stochastic bandit models for delayed conversions C Vernade, O Cappé, V Perchet arXiv preprint arXiv:1706.09186, 2017 | 112 | 2017 |
| Online learning in repeated auctions J Weed, V Perchet, P Rigollet Conference on Learning Theory, 1562-1583, 2016 | 109 | 2016 |
| Highly-smooth zero-th order online optimization F Bach, V Perchet Conference on Learning Theory, 257-283, 2016 | 103 | 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 | 81 | 2020 |
| Anytime optimal algorithms in stochastic multi-armed bandits R Degenne, V Perchet International Conference on Machine Learning, 1587-1595, 2016 | 69 | 2016 |
| Combinatorial semi-bandit with known covariance R Degenne, V Perchet Advances in Neural Information Processing Systems 29, 2016 | 60 | 2016 |
| Approachability, regret and calibration; implications and equivalences V Perchet arXiv preprint arXiv:1301.2663, 2013 | 59* | 2013 |
| 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 | 47 | 2020 |
| 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 | 47 | 2016 |
| Local differential privacy for regret minimization in reinforcement learning E Garcelon, V Perchet, C Pike-Burke, M Pirotta Advances in Neural Information Processing Systems 34, 10561-10573, 2021 | 44 | 2021 |
| Learning in repeated auctions T Nedelec, C Calauzènes, N El Karoui, V Perchet Foundations and Trends® in Machine Learning 15 (3), 176-334, 2022 | 41 | 2022 |
| 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 | 41* | 2014 |
| Learning to bid in revenue-maximizing auctions T Nedelec, N El Karoui, V Perchet International Conference on Machine Learning, 4781-4789, 2019 | 40* | 2019 |
| Fast rates for bandit optimization with upper-confidence frank-wolfe Q Berthet, V Perchet Advances in Neural Information Processing Systems 30, 2017 | 40 | 2017 |
| Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms. V Perchet Journal of Machine Learning Research 12 (6), 2011 | 38 | 2011 |
Philippe RigolletMassachusetts Institute of TechnologyVerified email at math.mit.edu
