Speaker: Kaiyi Ji, University at Buffalo.
Time: 15:00-16:00, Jul.25
Location: SIST 1A 200
Host: Xin Liu
Abstract:
Bilevel optimization (BO) has received increasing attention in modern machine learning (ML), and has become a theoretical foundation for designing efficient computational tools for various ML areas such as meta-learning, autoML, fair ML, continual learning, and etc. In the first part of this talk, I will propose a novel stochastic bilevel optimization algorithm named stocBiO, which features a sample-efficient hypergradient estimation via Hessian-vector computations and automatic differentiation. In the second part, I will present the convergence analysis for stocBiO and discuss its application in coreset selection for rehearsal based continual learning by proposing a new bilevel problem formulation.
Bio:
Kaiyi Ji is currently an assistant professor at the Department of Computer Science and Engineering of the University at Buffalo (UB), and is also an affiliated faculty with the Institute for Artificial Intelligence and Data Science (IAD). Dr. Ji was a postdoctoral research fellow at the Electrical Engineering and Computer Science Department of the University of Michigan, Ann Arbor, in 2022. Dr. Ji received the Ph.D. degree from the Electrical and Computer Engineering Department of The Ohio State University in December, 2021. Dr. Ji was a visiting student research collaborator at the department of Electrical Engineering, Princeton University. Previously Dr. Ji obtained the B.S. degree from the University of Science and Technology of China in 2016. Dr. Ji has worked at the intersection of optimization, machine learning and communication networks, on both the theory and application sides. His current major interest lies in bilevel optimization and its application to various machine learning areas including meta-learning, continual learning, hyperparameter optimization, etc. He received the prestigious Presidential Fellowship at OSU in 2020.
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