Alexandra Carpentier
Alexandra Carpentier
Uni Potsdam
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An optimal algorithm for the thresholding bandit problem
A Locatelli, M Gutzeit, A Carpentier
International Conference on Machine Learning, 1690-1698, 2016
Stochastic simultaneous optimistic optimization
M Valko, A Carpentier, R Munos
International Conference on Machine Learning, 19-27, 2013
Bandit theory meets compressed sensing for high dimensional stochastic linear bandit
A Carpentier, R Munos
Artificial Intelligence and Statistics, 190-198, 2012
Upper-confidence-bound algorithms for active learning in multi-armed bandits
A Carpentier, A Lazaric, M Ghavamzadeh, R Munos, P Auer
International Conference on Algorithmic Learning Theory, 189-203, 2011
Tight (lower) bounds for the fixed budget best arm identification bandit problem
A Carpentier, A Locatelli
Conference on Learning Theory, 590-604, 2016
Simple regret for infinitely many armed bandits
A Carpentier, M Valko
International Conference on Machine Learning, 1133-1141, 2015
Increased expression of regulatory Tr1 cells in recurrent hepatitis C after liver transplantation
A Carpentier, F Conti, F Stenard, L Aoudjehane, C Miroux, P Podevin, ...
American Journal of Transplantation 9 (9), 2102-2112, 2009
Adaptivity to smoothness in x-armed bandits
A Locatelli, A Carpentier
Conference on Learning Theory, 1463-1492, 2018
Revealing graph bandits for maximizing local influence
A Carpentier, M Valko
Artificial Intelligence and Statistics, 10-18, 2016
Extreme bandits
A Carpentier, M Valko
Advances in Neural Information Processing Systems 27, 2014
Two-sample tests for large random graphs using network statistics
D Ghoshdastidar, M Gutzeit, A Carpentier, U von Luxburg
Conference on Learning Theory, 954-977, 2017
Two-sample hypothesis testing for inhomogeneous random graphs
D Ghoshdastidar, M Gutzeit, A Carpentier, U Von Luxburg
The Annals of Statistics 48 (4), 2208-2229, 2020
Finite time analysis of stratified sampling for Monte Carlo
A Carpentier, R Munos
Advances in Neural Information Processing Systems 24, 2011
Rotting bandits are no harder than stochastic ones
J Seznec, A Locatelli, A Carpentier, A Lazaric, M Valko
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Uncertainty quantification for matrix compressed sensing and quantum tomography problems
A Carpentier, J Eisert, D Gross, R Nickl
High Dimensional Probability VIII, 385-430, 2019
Adaptive confidence sets for matrix completion
A Carpentier, O Klopp, M Löffler, R Nickl
Bernoulli 24 (4A), 2429-2460, 2018
Adaptivity to noise parameters in nonparametric active learning
A Locatelli, A Carpentier, S Kpotufe
Proceedings of the 2017 Conference on Learning Theory, PMLR, 2017
Linear bandits with stochastic delayed feedback
C Vernade, A Carpentier, T Lattimore, G Zappella, B Ermis, M Brueckner
International Conference on Machine Learning, 9712-9721, 2020
Adaptive estimation of the sparsity in the Gaussian vector model
A Carpentier, N Verzelen
The Annals of Statistics 47 (1), 93-126, 2019
Automatic motor task selection via a bandit algorithm for a brain-controlled button
J Fruitet, A Carpentier, R Munos, M Clerc
Journal of neural engineering 10 (1), 016012, 2013
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