Dr. Levente Kocsis, Ph.D. University Maastricht, 2003, is the member of the Laboratory since January 2011. As a machine learning expert, he supervises related areas in the Lab. In 2006, together with Csaba Szepesvári, he introduced UCT (Upper Confidence bounds applied to Trees), a new algorithm that applies bandit ideas to guide Monte-Carlo planning.

Selected publications

  • András A. Benczúr, Levente Kocsis, Róbert Pálovics. Overview of Online Machine Learning in Big Data Streams. In: Sakr S., Zomaya A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham 2019
  • Location-aware online learning for top-k recommendation. R Pálovics, P Szalai, J Pap, E Frigó, L Kocsis, AA Benczúr. Pervasive and Mobile Computing, 2015.
  • I Hegedűs, Á Berta, L Kocsis, AA Benczúr, M Jelasity. Robust Decentralized Low-Rank Matrix Decomposition. ACM Transactions on Intelligent Systems and Technology (TIST) 7 (4), 62, 2015. Based on the Best paper. In Peer-to-Peer Computing (P2P), 14-th IEEE International Conference on (pp. 1-9). IEEE.
  • Gelly, S., Kocsis, L., Schoenauer, M., Sebag, M., Silver, D., Szepesvári, C., & Teytaud, O. (2012). The grand challenge of computer Go: Monte Carlo tree search and extensions. Communications of the ACM, 55(3), 106-113.