UTCS Colloquia/AI - Vibhav Gogate/UT Dallas, "Efficient Sampling-based Inference in Presence of Logical Structure", ACES 2.402

Contact Name: 
Jenna Whitney
Feb 10, 2012 11:00am - 12:00pm

There is a sign-up schedule for this event that can be found at


Type o

f Talk: UTCS Colloquia/AI

Speaker/Affiliation: Vibhav Gogate/UT Dallas

Talk Audience: UTCS Faculty, Graduate Students, Undergraduate Stude

nts and Outside Interested Parties

Date/Time: February 10, 2012, 11:

00 a.m.

Location: ACES 2.402

Host: Ray Mooney

Talk Title: Ef

ficient Sampling-based Inference in Presence of Logical Structure

The emerging field of statistical relational learning (SRL) se

eks to marry logical and probabilistic representation and reasoning techniq

ues. A good marriage is essential because many real world application domai

ns have both rich relational (i.e., logical) structure and large amount of
uncertainty. Although, great progress has been made in solving the repres

entational issues in SRL, progress in inference has been lacking. In this

talk, I''ll describe our ongoing attempt at achieving this much needed pro

gress. Specifically, I''ll focus on a class of simulation-based approximat

e inference technique called importance sampling and show how to substantia

lly improve its speed, scalability and accuracy by exploiting logical stru

cture. I''ll show that our new sampling algorithm is a special case of the

standard DPLL algorithm for Satisfiability (SAT) testing and theorem provin

g. This enables us to leverage efficient and highly scalable algorithms and
software from SAT and theorem proving communities for efficient inference

in SRL models. I''ll conclude by presenting results from the UAI 2010 appro

ximate inference challenge where our importance sampling method won first p

lace in many categories. (Joint work with Rina Dechter and Pedro Domingos)

Speaker Bio:
Vibhav Gogate is an assistant professor in the computer
science department at the University of Texas at Dallas. He got his Ph.D.

from University of California, Irvine in 2009 and then did a two-year post

doc at University of Washington. His research interests are in machine lear

ning and artificial intelligence with a focus on probabilistic graphical mo

dels and statistical relational learning. He has authored over 20 papers in
top-tier conferences and journals such as UAI, NIPS, AAAI, AISTATS and

the AI journal. He is a co-winner of the 2010 Uncertainty in Artificial Int

elligence (UAI) approximate inference challenge.