Published Papers
2020:
A. Carpentier, C. Duval, E. Mariucci. Total variation distance for discretely observed Lévy processes: a Gaussian approximation of the small jumps. To appear in Annales de l’Institut Henri Poincaré, 2020. arXiv
A. Manegueu, C. Vernade, A. Carpentier, M. Valko. Stochastic bandits with arm-dependent delays. To appear in ICML, 2020. arXiv
C. Vernade, A. Carpentier, T. Lattimore, G. Zappella, B. Ermis, M. Brückner. Linear bandits with Stochastic Delayed Feedback. To appear in ICML, 2020. arXiv
J. Cheshire, P. Menard, A. Carpentier. The Influence of Shape Constraints on the Thresholding Bandit Problem. To appear in COLT, 2020. Publisher website
A. Carpentier, S. Delattre, E. Roquain, N. Verzelen. Estimating minimum effect with outlier selection. To appear in the Annals of Statistics, 2020. arXiv
D. Ghoshdastidar, M. Gutzeit, A. Carpentier, U. von Luxburg. Two-sample Hypothesis Testing for Inhomogeneous Random Graphs. To appear in the Annals of Statistics, 2020. arXiv
A. Carpentier, and N. Verzelen. Optimal Sparsity Testing in Linear regression Model. To appear in the Bernoulli Journal, 2020. arXiv
2019:
A. Carpentier, N. Verzelen. Adaptive estimation of the sparsity in the Gaussian vector model.The Annals of Statistics 47.1: 93-126, 2019. arXivPublisher website
J. Achdou, J. Lam, A. Carpentier and G. Blanchard. A minimax near-optimal algorithm for adaptive rejection sampling. In Proceedings of Machine Learning Research (AISTATS), PMLR 98:94-126, 2019. arXiv Publisher website
J. Seznec, A. Locatelli, A. Carpentier, A. Lazaric, and M. Valko. Rotting bandits are no harder than stochastic ones. . In Proceedings of Machine Learning Research (AISTATS), PMLR 89:2564-2572, 2019. arXiv Publisher website
A. Locatelli, A. Carpentier and M. Valko. Active multiple matrix completion with adaptive confidence sets. In Proceedings of Machine Learning Research (AISTATS), PMLR 89:1783-1791, 2019. Publisher website
A.Carpentier, J.Eisert, D.Gross and R.Nickl. Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems. To appear in HDP, 2019. arXiv
A. Carpentier, O. Collier, L. Comminges, A. Tsybakov, Y. Wang. Minimax rate of testing in sparse linear regression. To appear in Automation and Remote Control, 2019. arXiv
2018:
A. Carpentier and A.K.H. Kim. An iterative hard thresholding estimator for low rank matrix recovery with explicit limiting distribution. Statistica Sinica, 28:1371-1393, 2018. arXivPublisher website
G. Blanchard, A. Carpentier, M. Gutzeit. Minimax Euclidean Separation Rates for Testing Convex Hypotheses in R^d. Electronic Journal of Statistics, 12.2:3713-3735, 2018, 2018. arXiv
A. Locatelli and A. Carpentier. Adaptivity to Smoothness in X-armed bandits. In Proceedings of Machine Learning Research (COLT), PMLR 75:1463-1492, 2018. Publisher website
A. Carpentier. One and two sided composite-composite tests in Gaussian mixture models. In Mathematisches Forschungsinstitut Oberwolfach Report No. 12/2018, pp21:22, 2018. Publisher website
An Adaptive Strategy for Active Learning with Smooth Decision Boundaryn the Proceedings of Machine Learning Research (ALT) 83:547-571, 2018. Publisher website arXiv
2017:
A. Locatelli, A. Carpentier and S. Kpotufe. Adaptivity to Noise Parameters in Nonparametric Active Learning. In the Proceedings of Machine Learning Research (COLT), 65:954-977, 2017. arXiv Publisher website
D. Ghoshdastidar, U. von Luxburg, M. Gutzeit and A. Carpentier. Two-Sample Tests for Large Random Graphs using Network Statistics. In the Proceedings of Machine Learning Research (COLT), 65:1383-1416, 2017. Publisher website arXiv
2016:
A. Carpentier and A. Locatelli. Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem. In the Journal of Machine Learning Research W&CP (COLT), 49:115, 2016. Publisher website arXiv
A. Locatelli, M. Gutzeit, A. Carpentier. An optimal algorithm for the Thresholding Bandit Problem.In the Journal of Machine Learning Research W&CP (ICML), 48:16901698, 2016. Publisher website
A. Erraqabi, M. Valko, A. Carpentier, O. Maillard. Pliable Rejection Sampling. In the Journal of Machine Learning Research W&CP (ICML), 48:21212129, 2016. Publisher website
A. Carpentier and M. Valko. Revealing Graph Bandits for Maximizing Local Influence. In the Journal of Machine Learning Research W&CP (AISTATS), 51:1018, 2016. Publisher website arXiv
A. Carpentier, O. Klopp, M. Loeffler. Constructing confidence sets for the matrix completion problem. In the proceedings of Advances in Nonparametric Statistics - 3 rd ISNPS, Avignon, France, 2016. arXiv
A. Carpentier and T. Schlueter. Learning Relationships between data obtained independently. In the Journal of Machine Learning Research W&CP (AISTATS), 51:658666, 2016. Publisher website arXiv
2015:
Alexandra Carpentier and Richard Nickl. On signal detection and confidence sets for low rank inference problems. In the Electronic Journal of Statistics, 9(2):2675-2688, 2015. Publisher website arXiv
A. Carpentier, R. Munos and A. Antos. Adaptive strategy for stratified Monte Carlo sampling. In the Journal of Machine Learning Research, 16(Nov):22312271, 2015. Publisher website
A. Carpentier and M. Valko. Simple regret for infinitely many armed bandits. In Journal of Machine Learning Research W&CP (ICML) Volume 37. Publisher website arXiv
A. Carpentier. Testing the regularity of a smooth signal. In the Bernoulli Journal, 21(1):465-488, 2015. Publisher website arXiv
A. Carpentier. Uncertainty quantification for high dimensional linear problems. In Mathematisches Forschungsinstitut Oberwolfach Report No. 26/2015, pp1474:1476, 2015. Publisher website
A. Carpentier. Implementable confidence sets in high dimensional regression. In Journal of Machine Learning Research W&CP (AISTATS) 38: 120-128, 2012, 2015. Publisher website arXiv
2014:
A. Carpentier and A.K.H. Kim. Honest and adaptive confidence interval for the tail coefficient in the Pareto model. In the Electronic Journal of Statistic, 8(2), pp. 2066-2110, 2014. Publisher website arXiv
A. Carpentier and M. Valko. Extreme Bandits. In Advances in Neural Information Processing Systems (NIPS) pp. 1089-1097, 2014. Publisher website
A. Carpentier and A.K.H. Kim. Adaptive and minimax optimal estimation of the tail coefficient. In Statistica Sinica, 25(3):1133-1144, 2014. Publisher website arXiv
A. Carpentier and R. Munos. Minimax Number of Strata for Online Stratified Sampling : the Case of Noisy Samples. Theoretical Computer Science, 558, 77-106, 2014. Publisher website
2013:
A. Carpentier. Honest and adaptive confidence sets in Lp. In the Electronic Journal of Statistics, volume 7, pp. 2875-2923, 2013. Publisher website
E.M. Thomas, M. Clerc, A. Carpentier, E. Daucé, D. Devlaminck, R. Munos. Optimizing P300-speller sequences by RIP-ping groups apart. In 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 1062-1065, IEEE, 2013. HAL Publisher website
A. Carpentier and R. Munos. Toward Optimal Stratification for Stratified Monte-Carlo Integration. In Journal of Machine Learning Research W&CP (ICML), vol. 28 (2), pp. 28-36, 2013. Publisher website arXiv
M. Valko, R. Munos and A. Carpentier. Stochastic Simultaneous Optimistic Optimization. In Journal of Machine Learning Research W&CP (ICML), vol. 28 (2), pp. 19-27, 2013. Publisher website
J. Fruitet, A. Carpentier, R. Munos and M. Clerc. Automatic motor task selection via a bandit algorithm for a brain-controlled button. In Journal of Neural Engineering 10(1), 016012, 2013. Publisher website
2012:
A. Carpentier and R. Munos. Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions. In Advances in Neural Information Processing Systems (NIPS), pp. 251-259, 2012. Publisher website arXiv
O. A. Maillard, and A. Carpentier. Online allocation and homogeneous partitioning for piecewise constant mean-approximation. In Advances in Neural Information Processing Systems (NIPS), 2012. Publisher website HAL
J. Fruitet, A. Carpentier, R. Munos and M. Clerc. Bandit Algorithms boost motor-task selection for Brain Computer Interfaces. In Advances in Neural Information Processing Systems (NIPS), pp. 449-457, 2012. Publisher website
A. Carpentier and R. Munos. Minimax Number of Strata for Online Stratified Sampling given Noisy Samples. In Algorithmic Learning Theory (ALT), pp. 229-244, 2012. Publisher website
A. Carpentier and R. Munos. Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit. In Journal of Machine Learning Research W&CP (AISTATS) 22: 190-198, 2012. Publisher website arXiv
2011:
A. Carpentier and R. Munos. Finite time analysis of stratified sampling for monte carlo. In Advances in Neural Information Processing Systems (NIPS), pp. 1278-1286, 2011. Publisher website
A. Carpentier, O. A. Maillard, and R. Munos. Sparse recovery with brownian sensing. In Advances in Neural Information Processing Systems (NIPS), pp. 1782-1790, 2011. Publisher website
\A. Carpentier, A. Lazaric, M. Ghavamzadeh, R. Munos and P. Auer. Upper Confidence Bounds Algorithms for Active Learning in Multi-Armed Bandits. In Algorithmic Learning Theory (ALT), pp. 189-203, 2011. Publisher website HAL
2010:
G. Guillot and A. Carpentier-Skandalis. On the informativeness of dominant and co-dominant genetic markers for Bayesian supervised clustering. The Open Statistics and Probability Journal, 2010. Publisher website