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This website has moved to http://odalricambrymmaillard.wordpress.com.
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On these pages, you will find my research activities in the wide fields of Mathematics > Statistical Theory and Computer Science > Machine Learning.

On the right panel, you will find further information regarding my research activity. You may want to read and comments on my publications, subscribe to the Probabilty and Statistics news mailing list, or follow much more interesting links.

Have a good day.
August 6 2014 4 06 /08 /August /2014 16:51
Concentration inequalities for sampling without replacement.
Rémi Bardenet, Odalric-Ambrym Maillard. In Bernoulli Journal, 2014. Abstract: Concentration inequalities quantify the deviation of a random variable from a fixed value. In spite of numerous applications, such as opinion surveys or ecological counting...
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June 16 2014 2 16 /06 /June /2014 14:39
Latent Bandits
Odalric-Ambrym Maillard, Shie Mannor In International Conference on Machine Learning, 2014. Abstract: We consider a multi-armed bandit problem where the reward distributions are indexed by two sets –one for arms, one for type– and can be partitioned into...
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October 1 2013 3 01 /10 /October /2013 13:30
Robust Risk-averse Multi-armed Bandits
Odalric-Ambrym Maillard In Algorithmic Learning Theory, 2013. Abstract: We study a variant of the standard stochastic multi-armed bandit problem when one is not interested in the arm with the best mean, but instead in the arm maximizing some coherent...
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September 1 2013 1 01 /09 /September /2013 16:35
Kullback-Leibler Upper Confidence Bounds for Optimal Sequential Allocation.
Olivier Cappé, Aurélien Garivier, Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz. In The Annals of Statistics, 2013. Abstract: We consider optimal sequential allocation in the context of the so-called stochastic multi-armed bandit model. We describe...
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March 14 2013 5 14 /03 /March /2013 10:57
Competing with an Infinite Set of Models in Reinforcement Learning
Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko,Ronald Ortner. In International Conference on Artificial Intelligence and Statistics, 2013. Abstract: We consider a reinforcement learning setting where the learner also has to deal with the problem...
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February 28 2013 5 28 /02 /February /2013 19:30
Optimal regret bounds for selecting the state representation in reinforcement learning.
Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko. In Proceedings of the 30th international conference on machine learning, ICML 2013, 2013. Abstract: We consider an agent interacting with an environment in a single stream of actions,...
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February 28 2013 5 28 /02 /February /2013 19:22
Hierarchical Optimistic Region Selection driven by Curiosity.
Hierarchical Optimistic Region Selection driven by Curiosity.
By Odalric-Ambrym Maillard. In Proceedings of the 25th conference on advances in Neural Information Processing Systems, NIPS '12, 2012. Abstract: This paper aims to take a step forwards making the term ''intrinsic motivation'' from reinforcement learning...
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February 28 2013 5 28 /02 /February /2013 19:05
Online allocation and homogeneous partitioning for piecewise constant mean-approximation.
By Alexandra Carpentier and Odalric-Ambrym Maillard. In Proceedings of the 25th conference on advances in Neural Information Processing Systems, NIPS '12, 2012. Abstract: In the setting of active learning for the multi-armed bandit, where the goal of...
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February 28 2013 5 28 /02 /February /2013 17:44
Linear regression with random projections.
By Odalric-Ambrym Maillard and Rémi Munos, In Journal of Machine Learning Research 2012, vol:13, pp:2735-2772. Abstract: We investigate a method for regression that makes use of a randomly generated subspace G_P (of finite dimension P) of a given large...
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December 19 2011 2 19 /12 /December /2011 22:49
Selecting the State-Representation in Reinforcement Learning
This page is dedicated to start discussions about the article "Selecting the State-Representation in Reinforcement Learning". Feel free to post any comment, sugggestion, question, correction, extension... I will enjoy discussing this with you. Abstract:...
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