报告时间：2023年3月23日上午9：00-10：00
报告地点：腾讯会议（会议ID：957-240-457）
报告嘉宾：Yiyuan She
报告题目：Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction
报告摘要：
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction
Modern high-dimensional methods often adopt the “bet on sparsity” principle, while in supervised multivariate learning statisticians may face “dense” problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modeling and relaxes the stringent sparsity assumption in variable selection. In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning. Moreover, an efficient optimization algorithm is developed, which performs subspace learning and clustering with guaranteed convergence. The obtained fixed-point estimators, though not necessarily globally optimal, enjoy the desired statistical accuracy beyond the standard likelihood setup under some regularity conditions. Moreover, a new kind of information criterion, as well as its scale-free form, is proposed for cluster and rank selection, and has a rigorous theoretical support without assuming an infinite sample size. Extensive simulations and real-data experiments demonstrate the statistical accuracy and interpretability of the proposed method.
个人简介：
Yiyuan She received Ph.D. in Statistics from Stanford University in 2008 and is a professor at the Department of Statistics, Florida State University, Tallahassee, FL. His research interests are in the areas of high dimensional statistics, statistical machine learning, optimization, signal processing, robust statistics, multivariate statistics, and network science. He received Florida State University Developing Scholar Award and the NSF CAREER award. Currently, he serves as an Associate Editor for Statistica Sinika and Journal of the American Statistical Association. He is Fellow of ASA, Fellow of IMS, and an Elected Member of ISI.
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