Bayesian Optimization for Probabilistic Programs
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
Please contact us in case of a broken link from here
| Authors | Tuan Anh Le, Michael A. Osborne, Jan-Willem van de Meent, Frank Wood, Tom Rainforth |
| Journal/Conference Name | NeurIPS 2016 12 |
| Paper Category | Artificial Intelligence |
| 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 |
| Comment |
Copyright Researcher 2021