Bayesian Optimization for Probabilistic Programs
This provides a novel optimization tool for probabilistic programming users, addressing a fundamental bottleneck in model parameter estimation.
The authors developed the first general framework for marginal maximum a posteriori estimation in probabilistic programs, enabling optimization of evidence for any graphical model with respect to arbitrary variables. Their Bayesian optimization approach, which exploits source code directly, achieved significant performance improvements over existing packages.
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.