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Colloquium - Kearns

Quantifying Induction: Some Recent Developments in the Theory of Machine Learning
AT&T Laboratories Research

The exchange of ideas between theoreticians and practitioners of machine learning has risen dramatically in the last decade. In this talk, I will sketch developments in two of the topics in which this cross-fertilization has been the most effective and interesting: the study of learning curves, which quantifies the rate at which a learning algorithm generalizes from empirical data, and the framework known as boosting, which has led to the discovery of new algorithms and shed light on some classical heuristics. Along the way, I will highlight some of the many areas that have had direct and powerful impact on modern machine learning, including information theory, statistical mechanics, and combinatorics.

Refreshments will be served immediately before the talk at 3:30pm.
Hosted by Satinder Singh.

Department of Computer Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
May 5, 2012 (14:13)