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Volkan Cevher

 

 

Thursday 12th June 2014

Time: 11am

 

Basement Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

Composite self-concordant minimization

 

We propose a variable metric framework for minimizing the sum of a self-concordant function and a possibly non-smooth convex function endowed with a computable proximal operator. We theoretically establish the convergence of our framework without relying on the usual Lipschitz gradient assumption on the smooth part. An important highlight of our work is a new set of analytic step-size selection and correction procedures based on the structure of the problem. We describe concrete algorithmic instances of our framework for several interesting large-scale applications, such as graph learning, Poisson regression with total variation regularization, and heteroscedastic LASSO.

Bio:
Volkan Cevher (volkan.cevher@epfl.ch) received the B.S. (valedictorian) degree in electrical engineering in 1999 from Bilkent University in Ankara, Turkey, and he received the Ph.D. degree in Electrical and Computer Engineering in 2005 from the Georgia Institute of Technology in Atlanta. He held research scientist positions at the University of Maryland, College Park from 2006 to 2007 and at Rice University in Houston, Texas, from 2008 to 2009. Currently, he is an Assistant Professor at the Swiss Federal Institute of Technology Lausanne with a complimentary appointment at the Electrical and Computer Engineering Department at Rice University. His research interests include signal processing theory, machine learning, graphical models, and information theory. He received a Best Paper Award at SPARS in 2009 and an ERC StG in 2011.

Link:
to the document containing the technical parts of the presentation: http://arxiv.org/abs/1308.2867