Csaba Szepesvári is a Canada CIFAR AI Chair at Amii and a professor in the Computing Science Department of the University of Alberta. He is a Senior Staff Research Scientist at DeepMind in Edmonton, Alberta.
Szepesvári works on reinforcement learning theory, creating and analyzing algorithms that learn efficiently and effectively while interacting with their environments in a sequential manner. He is interested in problems when a machine continuously interacts with its environment while trying to discover autonomously a good way of interacting with it. These interactive online learning problems are studied in various disciplines, such as within control theory under the name “dual control”, or within machine learning itself in the area of reinforcement learning.
- European Laboratory for Learning and Intelligent Systems, Fellow, 2019
- Test of Time Award, ECML/PKDD, 2016
- UAI Best Paper Runner-Up Award, 2014
- ICML Excellent Paper Award, 2014
- Inspirational Instructor Award, Interdepartmental Science Students’ Society of the University of Alberta, 2012
Lattimore, T., & Szepesvári, C. (2020). Bandit algorithms. Cambridge University Press.
Abbasi-Yadkori, Y., Pál, D., & Szepesvári, C. (2011). Improved algorithms for linear stochastic bandits. Advances in neural information processing systems, 24, 2312-2320.
Bubeck, S., Munos, R., Stoltz, G., & Szepesvári, C. (2011). X-Armed Bandits. Journal of Machine Learning Research, 12(5).
Szepesvári, C. (2010). Algorithms for reinforcement learning. Synthesis lectures on artificial intelligence and machine learning, 4(1), 1-103.
Kocsis, L., & Szepesvári, C. (2006). Bandit based monte-carlo planning. In European conference on machine learning (pp. 282-293). Springer, Berlin, Heidelberg.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.