ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. (arXiv:1901.11515v1 [cs.LG])

Optimizing an expensive-to-query function is a common task in science and
engineering, where it is beneficial to keep the number of queries to a minimum.
A popular strategy is Bayesian optimization (BO), which leverages probabilistic
models for this task. Most BO today uses Gaussian processes (GPs), or a few
other surrogate models. However, there is a broad set of Bayesian modeling
techniques that we may want to use to capture complex systems and reduce the
number of queries. Probabilistic programs (PPs) are modern tools that allow for
flexible model composition, incorporation of prior information, and automatic
inference. In this paper, we develop ProBO, a framework for BO using only
standard operations common to most PPs. This allows a user to drop in an
arbitrary PP implementation and use it directly in BO. To do this, we describe
black box versions of popular acquisition functions that can be used in our
framework automatically, without model-specific derivation, and show how to
optimize these functions. We also introduce a model, which we term the Bayesian
Product of Experts, that integrates into ProBO and can be used to combine
information from multiple models implemented with different PPs. We show
empirical results using multiple PP implementations, and compare against
standard BO methods.

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