Jean-Yves Audibert

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Jean-Yves Audibert, a French statistician, computer scientist, and lecturer at École Normale Supérieure de Cachan and École nationale des ponts et chaussées. His research topics include machine learning, specially Probably approximately correct (PAC) learning, Multi-armed bandits, Non-parametric statistics, Sequential prediction with limited feedback and Computer vision. Jean-Yves Audibert is contributor to the Go playing program Mogo, using Monte-Carlo Tree Search which uses patterns in the simulations and improvements in UCT.

=Selected Publications=

2004 ...

 * Jean-Yves Audibert (2004). PAC-Bayesian Statistical Learning Theory. Ph.D. thesis, Université Paris VI, pdf, slides as pdf
 * Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári (2007). Tuning Bandit Algorithms in Stochastic Environments. pdf
 * Yizao Wang, Jean-Yves Audibert, Rémi Munos (2008). Algorithms for Infinitely Many-Armed Bandits,, Advances in Neural Information Processing Systems, pdf, Supplemental material - pdf
 * Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári (2009). Exploration-exploitation trade-off using variance estimates in multi-armed bandits. Theoretical Computer Science, 410:1876-1902, 2009, pdf

2010 ...

 * Jean-Yves Audibert (2010). PAC-Bayesian aggregation and multi-armed bandits. Habilitation thesis, Université Paris Est, pdf, slides as pdf
 * Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi (2013). Regret in Online Combinatorial Optimization. arXiv:1204.4710v2

=External Links=
 * Jean-Yves Audibert - Homepage

=References=

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