报告题目：The automatic sparsity of kernel feature selection
报 告 人：阮丰 助理教授 美国西北大学统计系
报告时间：2023年7月4日 10:00
报告地点：数学楼第一报告厅
校内联系方式：王培洁 wangpeijie@jlu.edu.cn
报告摘要：In this talk, we will describe a new sparsity-inducing technique based on minimization a family of kernels, terminologically called kernel-based feature selection. Unlike standard sparsification methods which rely on l_1 penalization, early stopping or post-processing, kernel feature selection achieves sparsity seemingly effortlessly in finite samples, bypassing the need of explicit regularizations. We will shed light on our current theoretical understanding of this empirically observed phenomenon that pervasively happens. As an application of this finding, we will illustrate its potential use in constructing new algorithms consistent for feature selection in nonparametric settings.
This is based on joint work with Michael I. Jordan, and Keli Liu.
报告人简介：Feng Ruan is currently an assistant professor at Department of Statistics and Data Science from Northwestern University. Previously, he obtained his Ph.D. in Statistics at Stanford, advised by John Duchi, and was a postdoctoral researcher in EECS at the University of California, Berkeley, advised by Professor Michael Jordan. His current research has three driving goals: (1) Build optimal statistical inferential procedures accounting for crucial resource constraints such as computation, privacy, etc. (2) Develop modeling and analytic tools that give a calculus for understanding generally solvable non-convex problems. (3) Design new objectives so that local algorithms achieve guaranteed performances for problems of combinatorial structures.
