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Csaba Szepesvári,
a Hungarian computer scientiest with research interests in applications of statistical techniques in AI, and Reinforcement Learning [1].

Csaba Szepesvári worked at the Computer and Automation Research Institute of the Hungarian Academy of Sciences, and is actually Associate Professor [2] at the Department of Computing Science, University of Alberta and is principal investigator of the RLAI [3] group.

In 2006, together with Levente Kocsis, Csaba Szepesvári introduced UCT (Upper Confidence bounds applied to Trees), a new algorithm that applies bandit ideas to guide Monte-Carlo planning [4].
Csaba Szepesvári [5]

Selected Publications

[6] [7]

1994 ...

2005 ...

2010 ...

External Links


  1. ^ Research Interests of Csaba Szepesvári
  2. ^ The Alberta Ingenuity Fund
  3. ^ Reinforcement Learning and Artificial Intelligence (RLAI)
  4. ^ Levente Kocsis, Csaba Szepesvári (2006). Bandit based Monte-Carlo Planning
  5. ^ Homepage of Csaba Szepesvári
  6. ^ On-line publications of Csaba Szepesvári
  7. ^ dblp: Csaba Szepesvári

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