Bayesian Optimization for Probabilistic Programs

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Authors Tuan Anh Le, Michael A. Osborne, Jan-Willem van de Meent, Frank Wood, Tom Rainforth
Journal/Conference Name NeurIPS 2016 12
Paper Category
Paper Abstract 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.
Date of publication 2017
Code Programming Language Multiple
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