Nombre de documents

21

CV de Odalric-Ambrym Maillard


Article dans une revue4 documents

  • Rémi Bardenet, Odalric-Ambrym Maillard. Concentration inequalities for sampling without replacement. Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, 2015, 21 (3), pp.1361-1385. <10.3150/14-BEJ605>. <hal-01216652>
  • Akram Baransi, Odalric-Ambrym Maillard, Shie Mannor. Sub-sampling for Multi-armed Bandits. Proceedings of the European Conference on Machine Learning, 2014, pp.13. <hal-01025651v2>
  • Odalric-Ambrym Maillard, Shie Mannor. Latent Bandits. JFPDA, 2014, pp.05. <hal-00990804>
  • Olivier Cappé, Aurélien Garivier, Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz. Kullback-Leibler Upper Confidence Bounds for Optimal Sequential Allocation. Annals of Statistics, Institute of Mathematical Statistics, 2013, 41 (3), pp.1516-1541. <hal-00738209v2>

Communication dans un congrès7 documents

  • Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko. Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. International Conference on Algorithmic Learning Theory (ALT), Oct 2014, Bled, Slovenia. Springer, 8776, pp.140-154, 2014, LNCS. <hal-01057562>
  • Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko. Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. ICML - 30th International Conference on Machine Learning, 2013, Atlanta, USA, United States. 28(1), pp.543-551, 2013, JMLR W&CP. <hal-00778586>
  • Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko, Ronald Ortner. Competing with an Infinite Set of Models in Reinforcement Learning. AISTATS, 2013, Arizona, United States. 31, pp.463-471, 2013, JMLR W&CP. <hal-00823230>
  • Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz. A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. Sham Kakade & Ulrike von Luxburg. 24th Annual Conference on Learning Theory : COLT'11, Jul 2011, Budapest, Hungary. pp.18, 2011. <inria-00574987v2>
  • Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko. Selecting the State-Representation in Reinforcement Learning. Neural Information Processing Systems, Dec 2011, Granada, Spain. 2011. <hal-00639483>
  • Odalric-Ambrym Maillard, Rémi Munos, Alessandro Lazaric, Mohammad Ghavamzadeh. Finite-Sample Analysis of Bellman Residual Minimization. Asian Conference on Machine Learning, 2010, Japan. 2010. <hal-00830212>
  • Odalric-Ambrym Maillard, Rémi Munos. Compressed Least-Squares Regression. NIPS 2009, Dec 2009, Vancouver, Canada. 2009. <inria-00419210v2>

Autre publication2 documents

  • Odalric-Ambrym Maillard, Shie Mannor. Latent Bandits.. Extended version of the paper accepted to ICML 2014 (paper and supplementary material). 2014. <hal-00926281>
  • Odalric-Ambrym Maillard. Robust Risk-averse Stochastic Multi-Armed Bandits. Extended version with supplementary material of the same paper submitted to the conference ALT 2013. 2013. <hal-00821670>

Pré-publication, Document de travail4 documents

  • Aditya Gopalan, Odalric-Ambrym Maillard, Mohammadi Zaki. Low-rank Bandits with Latent Mixtures. 2016. <hal-01400318>
  • Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard. Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem. 2016. <hal-01400320>
  • Odalric-Ambrym Maillard. Hierarchical Optimistic Region Selection driven by Curiosity. 2012. <hal-00740418>
  • Odalric-Ambrym Maillard, Alexandra Carpentier. Online allocation and homogeneous partitioning for piecewise constant mean-approximation. 22. 2012. <hal-00742893>

Thèse1 document

  • Odalric-Ambrym Maillard. APPRENTISSAGE SÉQUENTIEL : Bandits, Statistique et Renforcement.. Machine Learning [cs.LG]. Université des Sciences et Technologie de Lille - Lille I, 2011. English. <tel-00845410>

Rapport3 documents

  • Odalric-Ambrym Maillard, Rémi Munos. Adaptive Bandits: Towards the best history-dependent strategy. [Technical Report] 2011, pp.14. <inria-00574999>
  • Odalric-Ambrym Maillard, Rémi Munos. Brownian Motions and Scrambled Wavelets for Least-Squares Regression. [Technical Report] 2010, pp.13. <inria-00483017>
  • Odalric-Ambrym Maillard, Rémi Munos. Linear regression with random projections. [Technical Report] 2010, pp.22. <inria-00483014v2>