Algorithm

Engineering

Bayesian probability

Aerospace engineering

Statistics

Variance reduction

Convergence (economics)

Operating system

Range (aeronautics)

Economics

Bayesian optimization

Computer science

Mathematical optimization

Modular design

Mathematics

Artificial intelligence

Monte Carlo method

Economic growth

Optimization problem

Machine learning

arXiv (Cornell University)

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.

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization