报告题目：IEEE ITSOC Distinguished Lecturer Talk: Flexible Tensor Decompositions for Learning and Optimization

报告人：Anand D. Sarwate

报告时间：2025年7月31日（周四）15:30-17:00

报告地点：犀浦校区3号教学楼X30456

报告摘要：Many measurements or signals are multidimensional, or tensor-valued. To fit this data in existing machine learning pipelines, this data is vectorized, causing a blowup in the dimensionality. An alternative approach is to use tensor decompositions to create more structured models that respect the multidimensional structure. In this work we propose a family such structured decompositions, which we call low separation rank (LSR) tensor models. In the talk I will relate these to classical decompositions and show how the LSR model can balance model complexity and performance in supervised and unsupervised learning. Time permitting, we will describe applications of these ideas in other machine learning problems. This talk is based on joint work with Batoul Taki, Zahra Shakeri, Mohsen Ghassemi, Xin Li, and Waheed U. Bajwa.

报告人简介：Anand D. Sarwate is a professor in the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. He received B.S. degrees in mathematics and electrical engineering from MIT and a Ph.D. in electrical engineering from UC Berkeley. Prior to joining Rutgers he was a Research Assistant Professor at TTI-Chicago and a postdoc at the ITA Center at UC San Diego. His research interests include information theory, machine learning, signal processing, optimization, and privacy and security. Dr. Sarwate serves on the Board of Governors of the IEEE Information Theory Society (ITSOC) and is a ITSOC Distinguished Lecturer for 2024-2025.