Nicolò Cesa-Bianchi

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Nicolò Cesa-Bianchi, an Italian computer scientist and professor at department of computer science, University of Milan. His research interests include a wide range in the fields of machine learning and computational learning theory, such as reinforcement learning, game-theoretic learning, statistical learning theory, prediction with expert advice, and bandit problems. Along with Gábor Lugosi, he authored Prediction, Learning, and Games in 2006.

=Bandit Problems= In probability theory, the multi-armed bandit problem faces the tradeoff between exploitation of the slot machine that has the highest expected payoff and exploration to get more information about the expected payoffs of the other machines. The trade-off between exploration and exploitation is also topic in reinforcement learning. The gambler has to decide at time steps t = 1, 2, ... which of the finitely many available arms to pull. Each arm produces a reward in a stochastic manner. The goal is to maximize the reward accumulated over time. In 2002, along with Peter Auer and Paul Fischer, Nicolò Cesa-Bianchi introduced the UCB1 (Upper Confidence Bounds) bandit algorithm, which was applied as selection algorithm UCT to Monte-Carlo Tree Search as elaborated by Levente Kocsis and Csaba Szepesvári in 2006.

=Selected Publications=

1990 ...

 * Nicolò Cesa-Bianchi (1990). Learning the Distribution in the Extended PAC Model. ALT 1990
 * Peter Auer, Nicolò Cesa-Bianchi (1994). On-line learning with malicious noise and the closure algorithm. Algorithmic Learning Theory, LNAI, Springer
 * Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, Manfred K. Warmuth (1996). On-line Prediction and Conversion Strategies. Machine Learning, Vol. 25, No 1, pdf
 * Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert Schapire, Manfred K. Warmuth (1997). How to Use Expert Advice. Journal of the ACM, Vol. 44, No. 3, pdf
 * Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert Schapire (1998). Gambling in a rigged casino: The adversarial multi-arm bandit problem. NeuroCOLT2, pdf
 * Nicolò Cesa-Bianchi, Paul Fischer (1998). Finite-Time Regret Bounds for the Multiarmed Bandit Problem. ICML 1998, CiteSeerX

2000 ...

 * Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert Schapire (2002). The Nonstochastic Multiarmed Bandit Problem. SIAM Journal on Computing, Vol. 32, No. 1, 2001 pdf
 * Peter Auer, Nicolò Cesa-Bianchi, Paul Fischer (2002). Finite-time Analysis of the Multiarmed Bandit Problem. Machine Learning, Vol. 47, No. 2, pdf
 * Nicolò Cesa-Bianchi, Gábor Lugosi (2003). Potential-based Algorithms in On-line Prediction and Game Theory. Machine Learning, Vol. 51, pdf
 * Nicolò Cesa-Bianchi, Gábor Lugosi (2006). Prediction, Learning, and Games. Cambridge University Press
 * Nicolò Cesa-Bianchi (2009). Online discriminative learning: theory and applications. ASRU 2009

2010 ...

 * Fabio Vitale, Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella (2011). See the Tree Through the Lines: The Shazoo Algorithm. NIPS 2011, pdf
 * Nicolò Cesa-Bianchi (2011). The Game-Theoretic Approach to Machine Learning and Adaptation. ICAIS 2011
 * Nicolò Cesa-Bianchi (2011). Ensembles and Multiple Classifiers: A Game-Theoretic View. MCS 2011
 * Nicolò Cesa-Bianchi, Gábor Lugosi (2012). Combinatorial Bandits. Journal of Computer and System Sciences, Vol. 78, preprint as pdf
 * Sébastien Bubeck, Nicolò Cesa-Bianchi (2012). Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends in Machine Learning, Vol. 5, No. 1, pdf
 * Sébastien Bubeck, Nicolò Cesa-Bianchi, Gábor Lugosi (2013). Bandits With Heavy Tail. IEEE Transactions on Information Theory, Vol. 59, No. 11, arXiv:1209.1727v1
 * Nicolò Cesa-Bianchi (2015). Multi-armed Bandit Problem. Encyclopedia of Algorithms 2015

=External Links=
 * Nicolò Cesa-Bianchi
 * Nicolò Cesa-Bianchi from Wikipedia
 * Nicolò Cesa-Bianchi - Google Scholar Citations
 * Nicolò Cesa-Bianchi - University of Milan - VideoLectures.NET

=References=

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