[Intelligence Seminar] Talk of interest: Monday, 3/15, noon, GHC6115: Guy Lebanon on "Value of Labels in Unsupervised, Supervised, and Semisupervised Learning"

[Noah A Smith]

Machine Learning Lunch (http://www.cs.cmu.edu/~learning/)
Speaker: Guy Lebanon
Venue: GHC 6115
Date: Monday, March 15
Time: 12:00 noon
Food will be provided

Abstract:

I will describe two recent results regarding the value of labels in
classification and structured prediction. The first result describes
how margin-based classifiers (such as logistic regression and SVM) may
be trained without any labels whatsoever. This paradoxical statement
is true if the data dimensionality is high and the label marginal p(y)
is known. The second result derives the benefit of increasing the
number of labeled examples in semisupservised learning and of
different labeling policies in structured prediction.


Bio:

Guy Lebanon is an assistant professor of computing at the Georgia
Institute of Technology. His main research area is statistical
modelling and visualization of high dimensional discrete data such as
text documents and partially ranked data. Additional research
interests include privacy preservation in databases and social
networks and the use of non-Euclidean geometry in machine learning.

Prior to his current appointment at Georgia Tech, Dr. Lebanon was an
assistant professor of statistics and electrical and computer
engineering at Purdue University. He received his PhD in 2005 from
Carnegie Mellon University and BA, MS degrees from Technion - Israel
Institute of Technology, all in computer science.

Prof. Lebanon received the NSF CAREER Award, Purdue's Teaching for
Tomorrow Award, the 2004 LTI SRS Best Presentation Award and is a
Siebel scholar.