[Colloquium] Talk at TTI-C Next Week: 6/6/05, 12:00pm Tony Jebara

[Katherine Cumming]

Toyota Technological Institute at Chicago Talk
 
Guest Speaker:  Tony Jebara 
Columbia University Computer Science
URL:   <http://www1.cs.columbia.edu/~jebara/>
http://www1.cs.columbia.edu/~jebara/
When:  Monday, June 6th, 2005 @ 12:00pm  - Lunch Provided by TTI-C
Where:  TTI-C Conference Room
 
Title:  Tree Dependent Identically Distributed Learning
Abstract:
We view a dataset of points or samples as having an underlying, yet
unspecified, tree structure and exploit this assumption in learning
problems. Such a tree structure assumption is equivalent to treating a
dataset as being tree dependent identically distributed or tdid and
preserves exchangeability. This extends traditional iid assumptions on
data since each datum can be sampled sequentially after being
conditioned on a parent. Instead of hypothesizing a single best tree
structure, we infer a richer Bayesian posterior distribution over tree
structures from a given dataset. We compute this posterior over
(directed or undirected) trees via the Laplacian of conditional
distributions between pairs of input data points. This posterior
distribution is efficiently normalized by the Laplacian's determinant
and also facilitates novel maximum likelihood estimators, efficient
expectations and other useful inference computations. In a
classification setting, tdid assumptions yield a criterion that
maximizes the determinant of a matrix of conditional distributions
between pairs of input and output points. This leads to a novel
classification algorithm we call the Maximum Determinant Machine.
Unsupervised and supervised experiments are shown.

Joint work with Phil Long and Risi Kondor.

Brief Bio

Tony Jebara is an Assistant Professor of Computer Science at Columbia
University. He is Director of the Columbia Machine Learning Laboratory
whose research focuses upon machine learning, computer vision and
related application areas such as human-computer interaction. Jebara is
also a Principal Investigator at Columbia's Vision and Graphics Center.
He has published over 30 papers in the above areas including the book
Machine Learning: Discriminative and Generative (Kluwer). Jebara is the
recipient of the Career award from the National Science Foundation and
has also received honors for his papers from the International
Conference on Machine Learning and from the Pattern Recognition Society.
He has served as co-chair and program committee member for various
conferences and workshops. Jebara's research has been featured on
television (ABC, BBC, New York One, TechTV, etc.) as well as in the
popular press (Wired Online, Scientific American, Newsweek, Science
Photo Library, etc.). Jebara obtained his Bachelor's from McGill
University (at the McGill Center for Intelligent Machines) in 1996. He
obtained his Master's in 1998 and his PhD in 2002 both from the
Massachusetts Institute of Technology (at the MIT Media Laboratory). He
is currently a member of the IEEE, ACM and AAAI. Professor Jebara's
research and laboratory are supported in part by Microsoft, Alpha Star
Corporation and the National Science Foundation. 
If you have questions, or would like to meet the speaker, please contact
Katherine at 773-834-1994 or kcumming at tti-c.org. For information on
future TTI-C talks or events, please go to the TTI-C Events
<http://ttic.uchicago.edu/events/events_dyn.php>  page.  TTI-C:  1427
East 60th Street, Chicago, IL  60637.
 
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