Toyota AI Seminar: Efficient Implementation of and Inference in Probabilistic Programming Languages
Tuesday, January 18, 2011|
4:00pm - 5:30pm
3725 Beyster Bldg. (Stained Glass Conference Room)
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About the Event
Probabilistic programming languages simplify the development of probabilistic models by allowing programmers to specify a stochastic process using syntax that resembles modern programming languages. These languages allow programmers to freely mix deterministic and stochastic elements, resulting in tremendous modeling flexibility. The resulting programs define prior distributions: running the (unconditional) program forward many times results in a distribution over execution traces, with each trace generating a sample of data from the prior. The goal of inference in such programs is to reason about the posterior distribution over execution traces conditioned on a particular program output -- essentially "running the program backwards." In this talk, I will discuss a general technique for turning any programming language into a probabilistic programming language with an accompanying universal Markov chain Monte Carlo inference engine. The method allows the full use of all language constructs permitted by the original (non-probabilistic) language. I will illustrate the technique by discussing Stochastic Matlab, a new imperative probabilistic programming language, and will show examples of probabilistic programming applied to problems in planning, vision and machine learning. This is joint work with Noah Goodman, Andreas Stuhlmueller, and Joshua Tenenbaum.
David Wingate received a B.S. and M.S. in Computer Science from Brigham Young University in 2002 and 2004, and a Ph.D. in Computer Science from University of Michigan in 2008. He is currently a research scientist at MIT with a joint appointment in the Computational Cognitive Science group and Laboratory for Information Decision Systems. His research interests lie at the intersection of perception, control and learning. He has mostly recently focused on probabilistic programming and its application to reinforcement learning, dynamical systems modeling and machine learning.
Contact: Quang Duong
Open to: Public