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20 documents classés par : Date Titre Nom du premier auteur Type de documents Date de dépôt

1 - 2

Cox Process Learning Biau G., Cadre B., Paris Q. (06/05/2013) [hal-00820838 - version 1]

Cellular Tree Classifiers Biau G., Devroye L. (20/01/2013) [hal-00778520 - version 1]

A Primal Condition for Approachability with Partial Monitoring Mannor S., Perchet V., Stoltz G. (07/01/2013) [hal-00772056 - version 1]

Kullback-Leibler Upper Confidence Bounds for Optimal Sequential Allocation Cappé O., Garivier A., Maillard O.-A., Munos R., Stoltz G. (03/10/2012) [hal-00738209 - version 2]

New Insights into Approximate Bayesian Computation Biau G., Cérou F., Guyader A. (26/07/2012) [hal-00721164 - version 1]

Robust approachability and regret minimization in games with partial monitoring Mannor S., Perchet V., Stoltz G. (23/01/2012) [hal-00595695 - version 3]

An Affine Invariant $k$-Nearest Neighbor Regression Estimate Biau G., Devroye L., Dujmovic V., Krzyzak A. [hal-00655850 - version 2]

Statistique mathématique en action Rivoirard V., Stoltz G. Vuibert (Ed.) (2012) 448 [hal-00768869 - version 1]

PCA-Kernel Estimation Biau G., Mas A. Statistics and Risk Modeling (2012) 29, 19-46. [hal-00467013 - version 1]

Sparse single-index model Alquier P., Biau G. (05/10/2011) [hal-00556652 - version 2]

Lipschitz Bandits without the Lipschitz Constant Bubeck S., Stoltz G., Yu J. Y. Dans ALT 2011 - 22nd International Conference on Algorithmic Learning Theory (2011) (A venir) [hal-00595692 - version 2]

A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences Maillard O.-A., Munos R., Stoltz G. Dans 24th Annual Conference on Learning Theory : COLT'11 (2011) 18 [inria-00574987 - version 2]

Adaptive and Optimal Online Linear Regression on L1-balls Gerchinovitz S., Yu J. Y. [hal-00594399 - version 3]

X-Armed Bandits Bubeck S., Munos R., Stoltz G., Szepesvari C. Journal of Machine Learning Research 12 (2011) 1655-1695 [hal-00450235 - version 2]

Sparsity regret bounds for individual sequences in online linear regression Gerchinovitz S. Journal of Machine Learning Research 14 (2011) 729-769 [inria-00552267 - version 3]