Скачать или смотреть IDS Seminar - Best Arm Identification in Stochastic Bandits: Optimality, Complexity, and Robustness

Abstract: In this talk, we will provide recent results on three aspects of best arm identification (BAI) in stochastic multi-armed bandit problems. First, we present the recent optimality results for the general parameterized family of distributions. Next, we present a computationally-efficient framework for the fixed confidence setting. Finally, we consider the settings in which the rewards are possibly contaminated and provide two algorithms, a gap-based algorithm and one based on successive elimination, for the sub-Gaussian settings.

Bio. Ali Tajer received B.Sc. and M.Sc. degrees in Electrical Engineering from Sharif University of Technology, an M.A. degree in Statistics, and a Ph.D. degree in Electrical Engineering from Columbia University. He is currently an Associate Professor of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute. His research interests include mathematical statistics, statistical signal processing, and network information theory. His recent publications include an edited book entitled Advanced Data Analytics for Power Systems (Cambridge University Press, 2020). He received an NSF CAREER award in 2016 and AFRL Faculty Fellowship in 2019. He is currently serving as an Associate Editor for the IEEE Transactions on Information Theory and the IEEE Transactions on Signal Processing. In the past, he has served as an Editor for the IEEE Transactions on Communications, an Editor for the IEEE Transactions on Smart Grid, a Guest Editor for the IEEE Signal Processing Magazine, and a Guest Editor-in-Chief for the IEEE Transactions on Smart Grid.