Abstract: Message passing is the essential building block in many machine learning problems such as decentralized learning and graph neural networks. In this talk, I will introduce several innovative designs of message passing schemes that address the efficiency and security issues in machine learning. Specifically, first I will present a novel decentralized algorithm with compressed message passing that enables large-scale, efficient, and scalable distributed machine learning on big data. Then I will show how to significantly improve the security and robustness of graph neural networks by exploiting the structural information in data with a novel message passing design.

Short Bio: Xiaorui Liu is a Ph.D. candidate in the Department of Computer Science and Engineering at Michigan State University. His advisor is Prof. Jiliang Tang. His research interests include distributed and trustworthy machine learning, with a focus on big data and graph data. He was awarded the Best Paper Honorable Mention Award at ICHI 2019, MSU Engineering Distinguished Fellowship, and Cloud Computing Fellowship. He organized and co-presented four tutorials in KDD 2021, IJCAI 2021, and ICAPS 2021, and he has published innovative works in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, and AISTATS. More information can be found on his homepage
https://cse.msu.edu/~xiaorui/