Passive and Active Multi-Task Representation Learning

Abstract: Representation learning has been widely used in many applications. In this talk, I will present our work which uncovers when and why representation learning provably improves the sample efficiency, from a statistical learning point of view. Furthermore, I will talk about how to actively select the most relevant task to boost the performance.

Speaker Bio: Simon S. Du is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His research interests are broadly in machine learning, such as deep learning, representation learning, and reinforcement learning. Prior to starting as faculty, he was a postdoc at the Institute for Advanced Study of Princeton. He completed his Ph.D. in Machine Learning at Carnegie Mellon University. Simon's research has been recognized by an NSF CAREER award, a WAIC Yunfan Award, a Tencent AI Rhino-Bird Award, an Nvidia Pioneer Award, a AAAI New Faculty Highlights, and a Distinguished Dissertation Award honorable mention from CMU.