Vianney Perchet
Vianney Perchet
Crest, ENSAE & Criteo AI Lab
Email verificata su normalesup.org - Home page
Titolo
Citata da
Citata da
Anno
The multi-armed bandit problem with covariates
V Perchet, P Rigollet
The Annals of Statistics 41 (2), 693-721, 2013
1262013
Batched bandit problems
V Perchet, P Rigollet, S Chassang, E Snowberg
The Annals of Statistics 44 (2), 660-681, 2016
1002016
Bounded regret in stochastic multi-armed bandits
S Bubeck, V Perchet, P Rigollet
Conference on Learning Theory, 122-134, 2013
942013
Gaussian process optimization with mutual information
E Contal, V Perchet, N Vayatis
752014
SIC-MMAB: synchronisation involves communication in multiplayer multi-armed bandits
E Boursier, V Perchet
arXiv preprint arXiv:1809.08151, 2018
482018
Online learning in repeated auctions
J Weed, V Perchet, P Rigollet
Conference on Learning Theory, 1562-1583, 2016
482016
Highly-smooth zero-th order online optimization
F Bach, V Perchet
Conference on Learning Theory, 257-283, 2016
472016
Anytime optimal algorithms in stochastic multi-armed bandits
R Degenne, V Perchet
International Conference on Machine Learning, 1587-1595, 2016
442016
Approachability, regret and calibration; implications and equivalences
V Perchet
arXiv preprint arXiv:1301.2663, 2013
37*2013
Stochastic bandit models for delayed conversions
C Vernade, O Cappé, V Perchet
arXiv preprint arXiv:1706.09186, 2017
352017
Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms.
V Perchet
Journal of Machine Learning Research 12 (6), 2011
352011
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
31*2020
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
30*2014
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
282016
Calibration and internal no-regret with random signals
V Perchet
International Conference on Algorithmic Learning Theory, 68-82, 2009
28*2009
Fast rates for bandit optimization with upper-confidence Frank-Wolfe
Q Berthet, V Perchet
arXiv preprint arXiv:1702.06917, 2017
252017
Approachability of convex sets in games with partial monitoring
V Perchet
Journal of Optimization Theory and Applications 149 (3), 665-677, 2011
252011
Combinatorial semi-bandit with known covariance
R Degenne, V Perchet
Advances in Neural Information Processing Systems, 2972-2980, 2016
242016
Learning to bid in revenue-maximizing auctions
T Nedelec, N El Karoui, V Perchet
International Conference on Machine Learning, 4781-4789, 2019
18*2019
Gains and losses are fundamentally different in regret minimization: The sparse case
J Kwon, V Perchet
The Journal of Machine Learning Research 17 (1), 8106-8137, 2016
182016
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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