【数据科学名家讲坛】Learning Nonparametric Graphical Model on Heterogeneous Network-linked Data

2025-03-14 数据科学名家讲坛

SDS Colloquium Series

TopicLearning Nonparametric Graphical Model on Heterogeneous Network-linked Data
SpeakerJunhui WANG, Professor and Chair, Department of Statistics, Chinese University of Hong Kong
HostYongtao GUAN, Presidential Chair Professor, School of Data Science, CUHK-Shenzhen
Date14 March (Friday), 2025
Time11:00 AM - 12:00 PM, Beijing Time
FormatOnsite
Venue103 Meeting Room, Daoyuan Building
LanguageEnglish

Abstract

Graphical models have been popularly used for capturing conditional independence structure in multivariate data, which are often built upon independent and identically distributed observations, limiting their applicability to complex datasets such as network-linked data. In this talk, we introduce a nonparametric graphical model that addresses these limitations by accommodating heterogeneous graph structures without imposing any specific distributional assumptions. The introduced estimation method effectively integrates network embedding with nonparametric graphical model estimation. It further transforms the graph learning task into solving a finite-dimensional linear equation system by leveraging the properties of vector-valued reproducing kernel Hilbert space. We will also discuss theoretical properties of the proposed method in terms of the estimation consistency and exact recovery of the heterogeneous graph structures. Its effectiveness is also demonstrated through a variety of simulated examples and a real application to the statistician coauthorship dataset.

Biography

Prof. Junhui Wang is Professor and Chair of Department of Statistics at the Chinese University of Hong Kong. He received his B.S. in Probability and Statistics from Peking University, and Ph.D. in Statistics from University of Minnesota. His research interests include statistical machine learning and its applications in biomedicine, economics, finance and information technology. He has actively published in leading statistics and machine learning journals and conferences, including JASA, Biometrika, JMLR, NeurIPS, KDD and WWW. He also serves as Associate Editor for many statistics journals, including JASA, AoAS, JCGS and Statistica Sinica.