報(bào)告題目：IEEE ITSOC Distinguished Lecturer Talk: Flexible Tensor Decompositions for Learning and Optimization

報(bào)告人：Anand D. Sarwate

報(bào)告時(shí)間：2025年7月31日（周四）15:30-17:00

報(bào)告地點(diǎn)：犀浦校區(qū)3號(hào)教學(xué)樓X30456

報(bào)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.

報(bào)告人簡(jiǎn)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.