Date: Friday, November 1, 2024

Time: 3:00 p.m. - 4:00 p.m.

Location: Centergy Building 5126. The associated zoom link is: https://gatech.zoom.us/j/98414722872 

Speaker: Wenlong Mou

Speakers' Title: Assistant Professor in the Department of Statistical Sciences at University of Toronto

Seminar Title: Structure-driven design of reinforcement learning algorithms: a tale of bootstrapping and rollout

Abstract: Reinforcement learning (RL) is emerging as a powerful tool for adaptive decision-making in dynamic environments, offering potential for applications in signal processing, communications, and control systems. A key challenge in RL is learning value functions efficiently, which plays a critical role in optimizing decision policies. Over the years, a diverse range of RL algorithms has been proposed, but at their core, two foundational principles stand out: bootstrapping and rollout. Despite their success, finding the optimal trade-off between these principles in practical applications remains elusive, with current theoretical guarantees often falling short of providing actionable insights.

Bio: Wenlong Mou is an Assistant Professor in the Department of Statistical Sciences at University of Toronto. In 2023, he received his Ph.D. degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley. Prior to Berkeley, he received his B.Sc. degree in Computer Science and B.A. degree in Economics, both from Peking University. Wenlong's research interests include machine learning theory, mathematical statistics, optimization, and applied probability. He is particularly interested in designing optimal statistical methods that enable optimal data-driven decision making, powered by efficient computational algorithms. His works have been published in many leading journals in the area of statistical machine learning. His research has been recognized by the INFORMS Applied Probability Society as a Best Student Paper finalist.