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Jean-Yves Audibert ^[1]

Jean-Yves Audibert,
a French statistician, computer scientist, and lecturer at École Normale Supérieure de Cachan and École nationale des ponts et chaussées ^[2]. His research topics include machine learning, specially Probably approximately correct (PAC) learning, Multi-armed bandits, Non-parametric statistics, Sequential prediction ^[3] 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 ^[4].

Selected Publications

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

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

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