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Poster
Bayesian Optimization for Probabilistic Programs
Thomas Rainforth · Tuan Anh Le · Jan-Willem van de Meent · Michael A Osborne · Frank Wood

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #115
in Wednesday Posters »

We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evidence of any probabilistic program, and therefore of any graphical model, can be optimized with respect to an arbitrary subset of its sampled variables. To carry out this optimization, we develop the first Bayesian optimization package to directly exploit the source code of its target, leading to innovations in problem-independent hyperpriors, unbounded optimization, and implicit constraint satisfaction; delivering significant performance improvements over prominent existing packages. We present applications of our method to a number of tasks including engineering design and parameter optimization.

Keywords: (Other) Probabilistic Models and Methods · (Other) Optimization · (Other) Bayesian Inference · Gaussian Processes

Author Information

Thomas Rainforth (University of Oxford)
Tuan Anh Le (University of Oxford)
Jan-Willem van de Meent (University of Oxford)
Michael A Osborne (U Oxford)
Frank Wood (University of British Columbia)

Dr. Wood is an associate professor in the Department of Engineering Science at the University of Oxford. Before that he was an assistant professor of Statistics at Columbia University and a research scientist at the Columbia Center for Computational Learning Systems. He formerly was a postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University College London. He holds a PhD from Brown University (’07) and BS from Cornell University (’96), both in computer science. Dr. Wood is the original architect of both the Anglican and Probabilistic-C probabilistic programming systems. He conducts AI-driven research at the boundary of probabilistic programming, Bayesian modeling, and Monte Carlo methods. Dr. Wood holds 6 patents, has authored over 50 papers, received the AISTATS best paper award in 2009, and has been awarded faculty research awards from Xerox, Google and Amazon. Prior to his academic career he was a successful entrepreneur having run and sold the content-based image retrieval company ToFish! to AOL/Time Warner and served as CEO of Interfolio.

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