PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
Sham Kakade, Ohad Shamir, Karthik Sridharan and Ambuj Tewari
In: AISTATS 2010(2010).

Abstract

The versatility of exponential families, along with their attendant convexity properties, make them a popular and effective statistical model. A central issue is learning these models in high-dimensions when the optimal parameter vector is sparse. This work characterizes a certain strong convexity property of general exponential families, which allows their generalization ability to be quantified. In particular, we show how this property can be used to analyze generic exponential families under L1 regularization.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6937
Deposited By:Ohad Shamir
Deposited On:01 June 2010