UMN Machine Learning Seminar

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations.

This week's speaker, Tianxi Li (University of Virginia) will be giving a talk titled "Diffusion Source Identification on Networks with Statistical Confidence." Please note that this week's seminar will be held from 12:30 p.m. - 1:30 p.m.

Abstract

Diffusion source identification on networks is a problem of fundamental importance in a broad class of applications, including rumor controlling and virus identification. Though this problem has received significant recent attention, most studies have focused only on very restrictive settings and lack theoretical guarantees for more realistic networks. We introduce a statistical framework for the study of diffusion source identification and develop a confidence set inference approach inspired by hypothesis testing. Our method efficiently produces a small subset of nodes, which provably covers the source node with any pre-specified confidence level without restrictive assumptions on network structures. Moreover, we propose multiple Monte Carlo strategies for the inference procedure based on network topology and the probabilistic properties that significantly improve the scalability. To our knowledge, this is the first diffusion source identification method with a practically useful theoretical guarantee on general networks. We demonstrate our approach via extensive synthetic experiments on well-known random network models and a mobility network between cities concerning the COVID-19 spreading. This is joint work with Quilan Dawkins and Haifeng Xu at UVA.

Biography

Tianxi Li is currently an assistant professor in the Department of Statistics at the University of Virginia. He obtained his Ph.D. from the University of Michigan in 2018. His research is mainly about statistical machine learning and statistical network analysis.