DSA Thrust Seminar | Improving Sample Efficiency of Online Temporal Difference Learning

Yangchen Pan recently obtained his Ph.D. in computer science from the University of Alberta. He is interested in a broad range of fundamental problems in artificial intelligence, such as improving sample efficiency of temporal difference learning, scalability issue in reinforcement learning, representation interference, and the very basic regression problems. He published some of his work at several well-known conferences in machine learning. As part of the academic service, he frequently provides official reviews for papers submitted to different prestigious venues, like AAAI, ICLR, ICML, AISTATS.