BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
This work provides a practical tool for researchers and practitioners in fields like machine learning and engineering, though it is incremental as it builds on existing Bayesian optimization methods.
The authors tackled the challenge of implementing efficient Bayesian optimization by introducing BoTorch, a framework that integrates Monte-Carlo acquisition functions and novel optimization techniques, resulting in improved sample efficiency compared to other libraries.
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.