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Learning Bounds for Domain Adaptation
John Blitzer · Yacov Crammer · Alex Kulesza · Fernando Pereira · Jennifer Wortman Vaughan

Mon Dec 03 10:30 AM -- 10:40 AM (PST) @ None #None

Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain. In the real world, though, we often wish to adapt a classifier from a source domain with a large amount of training data to different target domain with very little training data. In this work we give uniform convergence bounds for algorithms that minimize a convex combination of source and target empirical risk. The bounds explicitly model the inherent trade-off between training on a large but inaccurate source data set and a small but accurate target training set. Our theory also gives results when we have multiple source domains, each of which may have a different number of instances, and we exhibit cases in which minimizing a non-uniform combination of source risks can achieve much lower target error than standard empirical risk minimization.

Author Information

John Blitzer (Google)
Yacov Crammer (Technion)
Alex Kulesza (Google)
Fernando Pereira (Google)
Jenn Wortman Vaughan (Microsoft Research)

Jenn Wortman Vaughan is a Senior Researcher at Microsoft Research, New York City, where she studies algorithmic economics, machine learning, and social computing, with a frequent focus on prediction markets and crowdsourcing. Jenn came to MSR in 2012 from UCLA, where she was an assistant professor in the computer science department. She completed her Ph.D. at the University of Pennsylvania in 2009, and subsequently spent a year as a Computing Innovation Fellow at Harvard. She is the recipient of Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology, a National Science Foundation CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a handful of best paper or best student paper awards. In her "spare" time, Jenn is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which has been held each year since 2006.

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