报告人/Speaker: 王军辉，Department of Mathematics, City University of Hong Kong
报告题目/Title: Scalable Kernel-based Variable Selection with Sparsistency
时间/Date & Time: May 22, 2018, 15:30—16:30
Variable selection is central to sparse modeling, and many methods have been proposed under various model assumptions. In this talk, we will present a scalable framework for model-free variable selection in reproducing kernel Hilbert space (RKHS) without specifying any restrictive model. As op-posed to most existing model-free variable selection methods requiring fixed dimension, the proposed method allows dimension p to diverge with sample size n. The proposed method is motivated from the classical hard-threshold variable selection for linear models, but allows for general variable effects. It does not require specification of the underlying model for the response, which is appealing in sparse modeling with a large number of variables. The proposed method can also be adapted to various scenarios with specific model assumptions, including linear models, quadratic models, as well as additive models. The asymptotic estimation and variable selection consistencies of the proposed method are established in all the scenarios. If time permits, the extension of the proposed method beyond mean regression will also be discussed.
报告人简介/About the speaker:
Junhui Wang is now professor and associate head of Department of Mathematics at City University of Hong Kong. He received his B.S. in Probability and Statistics from Peking University in 2001, and Ph.D. in Statistics from University of Minnesota in 2006. His research interests include statistical machine learning, unstructured data analysis, big data analysis, model selection and variable selection, as well as their applications in biomedicine, finance and information technology. He has published 40+ research articles on leading statistics and machine learning journals, including 10+ on Journal of American Statistical Association, Biometrika, and Journal of Machine Learning Research. He also serves as associate editor of Annals of the Institute of Statistical Mathematics and Statistics and its interface.