Learning nonparametric graphical model on heterogeneous network-linked data

发布时间：2025-04-23 来源：威廉希尔williamhill 浏览次数：

Speaker:	王军辉	DateTime:	2025年4月29日（周二）上午11:00
Brief Introduction to Speaker: 香港中文大学		Place:	国交2号楼315会议室
Abstract:Graphical models have been popularly used for capturing conditional in- dependence structure in multivariate data, which are often built upon inde- pendent 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 ac- commodating heterogeneous graph structures without imposing any specific distributional assumptions. The introduced estimation method effectively integrates network embedding with nonparametric graphical model estima- tion. 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 throu...