[Statlist] Special Talk with Volkan Cevher, April 5th, 2011, 4pm, ETH Zurich, HG E 22

[Susanne Kaiser-Heinzmann]

Seminar für Statistik, ETH Zürich, Prof. Peter Bühlmann,

invited by Prof. Andreas Krause

   
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We would like to inform you about this special talk organized by

  Learning and Adaptive Systems, Department of Computer Science, ETH  
Zurich

  Tuesday, April 5 2011, 4pm in HG E 22, ETH Zurich

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with Volkan Cevher, EPFL

Title:

Compressible priors for high-dimensional statistics

  Abstract:

We develop a principled way of identifying probability distributions  
whose independent and identically distributed (iid) realizations are  
compressible, i.e., can be approximated as sparse. We focus on the  
context of Gaussian random underdetermined linear regression (GULR)  
problems, where compressibility is known to ensure the success of  
estimators exploiting sparse regularization. We prove that many of the  
conventional priors revolving around probabilistic interpretations of  
the p-norm (p<=1) regularization algorithms are in fact incompressible 
in the limit of large problem sizes. To show this, we identify  
nontrivial undersampling regions in GULR where the simple least  
squares solution almost surely outperforms an oracle sparse solution,  
when the data is generated from a prior such as the Laplace  
distribution. We provide rules of thumb to characterize large families  
of compressible and incompressible priors based on their second and  
fourth moments. Generalized Gaussians and generalized Pareto  
distributions serve as running examples for concreteness. We then  
conclude with a study of the statistics of wavelet coefficients of  
natural images in the context of compressible priors.



Bio:
Prof. Volkan Cevher received his BSc degree (valedictorian) in
Electrical Engineering from Bilkent University in 1999, and his PhD
degree in Electrical and Computer Engineering from Georgia Institute
of Technology in 2005. He held Research Scientist positions at
University of Maryland, College Park during 2006-2007 and at Rice
University during 2008-2009. Currently, he is an Assistant Professor
at Ecole Polytechnique Federale de Lausanne with joint appointment at
the Idiap Research Institute and a Faculty Fellow at Rice University.
His research interests include signal processing theory, machine
learning, graphical models, and information theory.



Time and location: April 5 2011, 4pm in HG E 22



More information:

Andreas Krause, http://las.ethz.ch

Volkan Cevher, http://lions.epfl.ch/

______________________________________________________________

ETH Zürich
Seminar für Statistik
Rämistrasse 101
CH-8092 Zürich
Tel: +41 446326518 Fax: +41 446321228
sekretariat at stat.math.ethz.ch







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