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Time--Data Tradeoffs by Aggressive Smoothing
John J Bruer · Joel A Tropp · Volkan Cevher · Stephen Becker

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

This paper proposes a tradeoff between sample complexity and computation time that applies to statistical estimators based on convex optimization. As the amount of data increases, we can smooth optimization problems more and more aggressively to achieve accurate estimates more quickly. This work provides theoretical and experimental evidence of this tradeoff for a class of regularized linear inverse problems.

Author Information

John J Bruer (Caltech)
Joel A Tropp (Caltech)

Joel A. Tropp is Professor of Applied & Computational Mathematics at California Institute of Technology. He earned the Ph.D. degree in Computational Applied Mathematics from the University of Texas at Austin in 2004. Prof. Tropp’s work lies at the interface of applied mathematics, electrical engineering, computer science, and statistics. The bulk of this research concerns the theoretical and computational aspects of sparse approximation, compressive sampling, and randomized linear algebra. He has also worked extensively on the properties of structured random matrices. Prof. Tropp has received several major awards for young researchers, including the 2007 ONR Young Investigator Award and the 2008 Presidential Early Career Award for Scientists and Engineers. He is also winner of the 32nd annual award for Excellence in Teaching from the Associated Students of the California Institute of Technology.

Volkan Cevher (EPFL)
Stephen Becker (University of Colorado)

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