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Principles of Learning Problem Design
John Langford · Alina Beygelzimer

Fri Dec 07 07:30 AM -- 06:30 PM (PST) @ Hilton: Sutcliffe A
Event URL: http://hunch.net/~learning-problem-design/ »

This workshop is about how to design learning problems. The task of designing learning problems can be understood as roughly parallel to the mechanism design problem within economics and game theory. Several recent examples of learning problem design that have appeared include: (1) Converting otherwise-unsupervised problems into supervised problems. (2) The use of algorithm created ancillary prediction problems for improved representation and predictive performance. (3) The method of reduction between learning tasks. This area is new and not entirely defined---it's our goal to bring together people interested in the topic, define what we do and don't understand, and attempt to define the principles of learning problem design. We welcome participation---email jl@hunch.net and beygel@gmail.com if interested.

Author Information

John Langford (Microsoft Research)

John Langford is a machine learning research scientist, a field which he says "is shifting from an academic discipline to an industrial tool". He is the author of the weblog hunch.net and the principal developer of Vowpal Wabbit. John works at Microsoft Research New York, of which he was one of the founding members, and was previously affiliated with Yahoo! Research, Toyota Technological Institute, and IBM's Watson Research Center. He studied Physics and Computer Science at the California Institute of Technology, earning a double bachelor's degree in 1997, and received his Ph.D. in Computer Science from Carnegie Mellon University in 2002. He was the program co-chair for the 2012 International Conference on Machine Learning.

Alina Beygelzimer (Yahoo Labs)

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