Research
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
December 7, 2020
Abstract
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BoTorch, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, auto-differentiation, and variance reduction techniques. BoTorch’s modular design facilitates flexible specification and optimization of probabilistic models written in PyTorch, simplifying implementation of new acquisition functions. Our approach is backed by novel theoretical convergence results and made practical by a distinctive algorithmic foundation that leverages fast predictive distributions, hardware acceleration, and deterministic optimization. We also propose a novel “one-shot” formulation of the Knowledge Gradient, enabled by a combination of our theoretical and software contributions. In experiments, we demonstrate the improved sample efficiency of BoTorch relative to other popular libraries.
AUTHORS
Written by
Maximilian Balandat
Brian Karrer
Daniel R. Jiang
Samuel Daulton
Benjamin Letham
Andrew Gordon Wilson
Eytan Bakshy
Research Topics
Machine Learning
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