[Statlist] Next talk: Tuesday, October 02, 2012, with Shaowei Lin, University of California Berkeley

[Cecilia Rey]

ETH and University of Zurich

Proff. P. Buehlmann -  L. Held - H.R. Kuensch -
M. Maathuis -  S. van de Geer - M. Wolf


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We are glad to announce the following talk
Tuesday, October 02, 2012, 15.15h, HG G 19.1

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by Shaowei Lin (University of California Berkeley)

Titel:
Understanding the curse of singularities in machine learning

Abstract
Many parameter estimation and integral approximation problems in machine learning suffer, not from the curse of dimensionality as commonly believed, but from the curse of singularities. A common way of overcoming such problems is regularization using sparse penalties. Recent developments in the learning theory of singular models might be the key to understanding this phenomenon. 
In this talk, we give a brief introduction to Sumio Watanabe's Singular Learning Theory, as outlined in his book "Algebraic Geometry and Statistical Learning Theory". We will learn how geometry and resolution of singularities help us approximate integrals efficiently.


The abstract is also to be found here:  http://stat.ethz.ch/events/research_seminar