CS Events Monthly View

Computer Science Department Colloquium

Tensor-structured dictionary learning: theory and algorithms


Download as iCal file

Tuesday, October 26, 2021, 11:00am


Speaker: Professor Anand Sarwate


Anand D. Sarwate is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers, the State University of New Jersey. He received a B.S. degree in Electrical Science and Engineering and a B.S. degree in Mathematics from MIT in 2002, an M.S. in Electrical Engineering from UC Berkeley in 2005 and a PhD in Electrical Engineering from UC Berkeley in 2008. Prof. Sarwate received the NSF CAREER award in 2015, and the Rutgers Board of Governors Research Fellowship for Scholarly Excellence in 2020. His interests are in information theory, machine learning, and signal processing, with applications to distributed systems, privacy and security, and biomedical research.

Location : Via Zoom

Event Type: Computer Science Department Colloquium

Abstract: Existing and emerging sensing technologies produce multidimensional, or tensor, data. The traditional approach to handling such data is to “flatten” or vectorize, the data. It is well-known that this ignores the multidimensional structure and that this structure can be exploited to improve performance. In this talk we study the problem of dictionary learning for tensor data, where we want to learn an efficient representations with low rank. A naïve approach to this problem would fail due to the large number of parameters to estimate in the dictionary. However, by looking at dictionaries that admit a low rank factorization the problem becomes tractable. We characterize how hard the statistical problem of estimating these dictionaries is and provide novel algorithms for learning them. Joint work with Z. Shakeri (EA), M. Ghassemi (JP Morgan Research), and W.U. Bajwa (Rutgers)


Rutgers University School of Arts and Sciences

Contact  Faculty Host: Jie Gao

Zoom link: https://rutgers.zoom.us/j/99942573861?pwd=Sk9GL05wQWFuMDJjakJneHp4T0I1UT09