Learning from Collective Preferences, Behavior, and Beliefs

VIDEO MISSING FIRST FIVE MINUTES OF SEMINAR DUE TO TECHNICAL DIFFICULTIES
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.

Jenn Wortman is a doctoral candidate at the University of Pennsylvania under the supervision of Michael Kearns. Before coming to Penn, she received her BA from Boston University and her MS from Stanford University, where she specialized in artificial intelligence. She is primarily interested in research problems that lie at the intersection of machine learning and other areas of computer science, including algorithms, computational economics, and secure computation. Her research has won a number of recent awards, including two consecutive best student paper awards at the Annual Conference on Learning Theory (COLT) and an outstanding paper award at the ACM Conference on Electronic Commerce (EC). During her graduate studies, Jenn spent a summer interning with the machine learning and microeconomics groups at Yahoo Research and another summer as a research intern at Google. In her spare time, she 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 will be held for the fourth time in 2009.