2012:
A. Carpentier and R. Munos. Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions. In Advances in Neural Information Processing Systems (NIPS), 2012.
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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.
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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), 2012.
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A. Carpentier and R. Munos. Minimax Number of Strata for Online Stratified Sampling given Noisy Samples. In Algorithmic Learning Theory (ALT), 2012.
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A. Carpentier and R. Munos. Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.
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2011:
A. Carpentier and R. Munos. Finite time analysis of stratified sampling for monte carlo. In Advances in Neural Information Processing Systems (NIPS), 2011.
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A. Carpentier, O. A. Maillard, and R. Munos. Sparse recovery with brownian sensing. In Advances in Neural Information Processing Systems (NIPS), 2011.
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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), 2011.
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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.
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