John Lafferty on Data, Computation and Risk (February 1)
Joseph Turian
2009-2010 Distinguished Lecture Series
Department of Computer Science, Columbia University
Monday, February 1st, 2010
11:00 am - Davis Auditorium, Schapiro Center
Three Rivers in Machine Learning: Data, Computation and Risk
John Lafferty
Carnegie Mellon University
Abstract:
Machine learning is a confluence of computer science and statistics
that is empowering technologies such as search engines, robotics, and
personalized medicine. Fundamentally, the goal of machine learning is
to develop computer programs that predict well, according to some
measure of risk or accuracy. The predictions should get better as more
historical data become available. The field is developing interesting
and useful frameworks for building such programs, which often demand
large computational resources. Theoretical analyses are also being
advanced to help understand the tradeoffs between computation, data,
and risk that are inherent in statistical learning. Two types of
results have been studied: the consistency and scaling behavior of
specific convex optimization procedures, which have polynomial
computational efficiency, and lower bounds on any statistically
efficient procedure, without regard to computational cost. This talk
will give a survey of some of these developments, with a focus on
structured learning problems for graphs and shared learning tasks in
high dimensions.
Biography:
John Lafferty is a professor in the Computer Science Department at
Carnegie Mellon University, with joint appointments in the Machine
Learning Department and the Department of Statistics. His research
interests include machine learning, statistical learning theory,
natural language processing, information theory, and information
retrieval. Prof. Lafferty received the Ph.D. in Mathematics from
Princeton University, where he was also a member of the Program in
Applied and Computational Mathematics. Before joining CMU in 1994, he
held a post-doctoral position at Harvard University, and was a
Research Staff Member at the IBM Thomas J. Watson Research Center in
Yorktown Heights, NY. More recently, he has held visiting positions at
the University of California, Berkeley and the University of
California, San Diego. Prof. Lafferty is an IEEE Fellow, has served as
co-director of CMU's new Ph.D. Machine Learning Ph.D. Program, and
currently serves as associate editor of the Journal of Machine
Learning Research and the Electronic Journal of Statistics.
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