2017:

Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike von Luxburg. Two-sample Tests for Random Graphs. arXiv

Andrea Locatelli, Alexandra Carpentier and Samory Kpotufe. Adaptivity to Noise Parameters in Nonparametric Active Learning. To appear in COLT 2017. arXiv Publisher website

Debarghya Ghoshdastidar, Ulrike von Luxburg, Maurilio Gutzeit and Alexandra Carpentier. Two-Sample Tests for Large Random Graphs using Network Statistics. To appear in COLT 2017. Publisher website arXiv

A. Carpentier, N. Verzelen. Adaptive estimation of the sparsity in the Gaussian vector model. arXiv preprint arXiv:1703.00167, 2017. arXiv

G. Blanchard, A. Carpentier, M. Gutzeit. Minimax Euclidean Separation Rates for Testing Convex Hypotheses in R^d. arXiv preprint arXiv:1702.03760, 2017. arXiv

A. Carpentier, O. Klopp, M. Loeffler, and R. Nickl. Adaptive Confidence Sets for Matrix Completion. To appear in Bernoulli, 2017. arXiv

A. Carpentier and A.K.H. Kim. An iterative hard thresholding estimator for low rank matrix recovery with explicit limiting distribution. To appear in Statistica Sinica, 2017. arXiv

A. Carpentier, O. Klopp, M. Loeffler. Constructing confidence sets for the matrix completion problem. arXiv preprint arXiv:1704.02760, 2017. 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), 49:1–15, 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), 48:1690–1698, 2016. Publisher website

A. Erraqabi, M. Valko, A. Carpentier, O. Maillard. Pliable Rejection Sampling. In the Journal of Machine Learning Research W&CP (ICML), 48:2121–2129, 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), 51:10–18, 2016. Publisher website arXiv

A. Carpentier and T. Schlueter. Learning Relationships between data obtained independently. In the Journal of Machine Learning Research W&CP (AISTATS), 51:658–666, 2016. Publisher website arXiv

2015:

Alexandra Carpentier, Jens Eisert, David Gross and Richard Nickl. Uncertainty Quantification for Matrix Compressed Sensing and Quantum Tomography Problems. arXiv preprint arXiv:1504.03234, 2015. arXiv

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):2231−2271, 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. 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. .pdf 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. .pdf 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. .pdf 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. .pdf 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. .pdf Publisher website

2012:

A. Carpentier. De l’échantillonnage optimal en grande et petite dimension. PhD Thesis, AFIA ex-aequo accessit 2013, (french machine learning/artificial intelligence second price). .pdf

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. .pdf 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. .pdf 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. .pdf 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. .pdf 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. .pdf 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. .pdf 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. .pdf 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. .pdf 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