[Intelligence Seminar] March 17: David Blei, Wean 5409, 3:30 - "Supervised and Relational Topic Models"

[Noah A Smith]

Intelligence Seminar

March 17, 2009
3:30 pm
Wean 5409
For meetings, contact Noah Smith (nasmith at cs.cmu.edu).

Supervised and relational topic models
David Blei
Princeton University

Abstract:

A surge of recent research in machine learning and statistics has
developed new techniques for finding patterns of words in document
collections using hierarchical probabilistic models.  These models are
called "topic models" because the discovered word patterns often
reflect the underlying topics that permeate the documents. Topic
models also naturally apply to data such as images and biological
sequences.

In this talk I will review the basics of topic modeling, and discuss
some recent extensions: supervised topic modeling and relational topic
modeling.  Supervised topic models allow us to use topics in a setting
where we seek both exploratory and predictive power.  Relational topic
models---which are built on supervised topic models---consider
documents interconnected in a graph.  These models can be used to
summarize a network of documents, predict links between them, and
predict words within them.

Joint work with Jonathan Chang and Jon McAuliffe.

Bio:

David Blei is an assistant professor in the Computer Science
department at Princeton University.  He received his Ph.D. in 2004
from U.C. Berkeley and was a postdoctoral researcher in the Department
of Machine Learning at Carnegie Mellon University.  His research
interests include graphical models, approximate posterior inference,
and nonparametric Bayesian statistics. He focuses on applications to
information retrieval and natural language processing.