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Activity Number: 406
Type: Invited
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318490 View Presentation
Title: Convex Regularization for High-Dimensional Tensor Regression
Author(s): Ming Yuan* and Garvesh Raskutti
Companies: University of Wisconsin and University of Wisconsin - Madison
Keywords: Tensor Regression

We present a general convex optimization approach for solving high-dimensional tensor regression problems under general low-dimensional structural assumptions. We consider using convex and weakly decomposable regularizers assuming the underlying tensor lies in an unknown low-dimensional subspace. Within this general framework, we derive upper bounds for convex methods applied to tensor regression for general dependent Gaussian design. Our framework provides rates of convergence for a number of fundamental statistical models of interests.

Authors who are presenting talks have a * after their name.

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