Solving Non-(Clarke)-Regular Optimization Problems in Statistical and Machine Learning
Department of Industrial and Systems Engineering
University of Minnesota
Thursday, March 4, 2021
View recording here
Although we have witnessed growing interests from the continuous optimization community in solving the nonconvex and nonsmooth optimization problems, most of the existing work focus on the Clarke regular objective or constraints so that many friendly properties hold. In this talk, we will discuss the pervasiveness of the non-(Clarke) regularity in the modern operations research and statistical estimation problems due to the complicated composition of nonconvex and nonsmooth functions. Emphasis will be put on the difficulties brought by the non-regularity both in terms of the computation and the statistical inference, and our initial attempts to overcome them.
Ying Cui is currently an assistant professor of the Department of Industrial and Systems Engineering at the University of Minnesota. Her research focuses on the mathematical foundation of data science with emphasis on optimization techniques for operations research, machine learning and statistical estimations. Prior to UMN, she was a postdoc research associate at the University of Southern California. She received her Ph.D from the Department of Mathematics at the National University of Singapore.