[Colloquium] TTI-C Talk: Jennifer Wortman, UPenn

[Julia MacGlashan]

REMINDER

When:             Tuesday, March 17th @ 11:00am (lunch will be provided
after talk)

Where:            6045 S Kenwood Ave, TTI-C Conference Room #526 (5th Floor)

Who:               Jennifer Wortman, University of Pennsylvania

Title:                Learning from Collective Preferences, Behavior, and
Beliefs

Machine learning has become one of the most active and exciting areas of
computer science research, in large part because of its wide-spread
applicability to problems as diverse as natural language processing, speech
recognition, spam detection, search, computer vision, gene discovery,
medical diagnosis, and robotics.  At the same time, the growing popularity
of the Internet and social networking sites like Facebook has led to the
availability of novel sources of data on the preferences, behavior, and
beliefs of massive populations of users.  Naturally, both researchers and
engineers are eager to apply techniques from machine learning in order to
aggregate and make sense of this wealth of collective information.  However,
traditional theories of learning fail to capture the complex issues that
arise in such settings, and as a result, many of the techniques currently
employed are ad hoc and not well understood.

A major goal of my research is to narrow this gap between theory and
practice by designing new learning models and algorithms to address and
illuminate problems commonly faced when aggregating local information from
large populations of users.  In this talk, I will discuss two specific
pieces of work that fall into this category.  In the first, we develop a
forecaster that is guaranteed to perform reasonably well compared to the
best expert in a population but simultaneously never any worse than the
average.  In the second, we investigate the computational complexity of
pricing in prediction markets, betting markets designed to aggregate
individuals' beliefs about the likelihood of future events, and propose an
approximation technique based on the previously unexplored connection
between prediction market prices and learning from expert advice.

Contact:          Sham Kakade, TTI-C		sham at tti-c.org
834-2550





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