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. The talks are every Thursday from 12 p.m. - 1 p.m. during the Summer 2021 semester.
This week's speaker, Yu Xiang (University of Utah) will be giving a talk.
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.
Yu Xiang is an Assistant Professor in Electrical and Computer Engineering at the University of Utah since July 2018. Prior to this, he was a postdoctoral fellow in Harvard John A. Paulson School of Engineering and Applied Sciences at Harvard University. He obtained his Ph.D. in Electrical and Computer Engineering from the University of California, San Diego in 2015. I received my B.E. with the highest distinction from the School of Telecommunications Engineering at Xidian University, Xi'an, China, in 2008. His current research interests include statistical signal processing, information theory, machine learning, and their applications to neuroscience and computational biology.