[Colloq] Talk - Wednesday, Jan. 10 - Brian Milch, MIT

[Rachel Kalweit]

College of Computer and Information Science Colloquium

Presents:
Brian Milch
MIT

Who will speak on:
“Probabilistic Models with Unknown Objects”

Wednesday, January 10, 2007
12:00 pm
366 West Village H
Northeastern University

Abstract:
Many AI problems, from tracking aircraft based on radar blips to 
extracting facts about people and events from text documents, involve 
making inferences about the real-world objects that underlie some data. 
In many cases, we do not know the number of underlying objects or the 
mapping between objects and observations. This talk will present a 
probabilistic modeling language, called Bayesian logic (or BLOG), which 
allows us to represent such scenarios in a natural way. A well-formed 
BLOG model fully defines a probability distribution over model 
structures of a first-order logical language; these "possible worlds" 
can contain varying numbers of objects with varying relations among 
them. I will also describe a Markov chain Monte Carlo algorithm for 
performing inference on BLOG models. This algorithm is novel in that it 
does a random walk not over fully specified possible worlds, but over 
partial world descriptions that instantiate only the relevant variables. 
I will present the results of applying this algorithm to identify the 
distinct publications referred to by a set of citation strings extracted 
from online papers.
The recent paper First-Order Probabilistic Languages: Into the Unknown 
gives a survey of first-order probabilistic languages.

Biography
Brian Milch is a postdoctoral researcher in the Computer Science and 
Artificial Intelligence Laboratory at MIT. He received his B.S. with 
honors in Symbolic Systems from Stanford University, where he worked 
with Prof. Daphne Koller. He then spent a year as a research engineer at 
Google before entering the computer science Ph.D. program at U.C. 
Berkeley. His thesis research, with Prof. Stuart Russell, was on 
representation and inference for models that combine probability and 
first-order logic. He received his Ph.D. in December 2006. He is also 
the recipient of an NSF Graduate Research Fellowship and a Siebel 
Scholarship.