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BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat · Brian Karrer · Daniel Jiang · Samuel Daulton · Ben Letham · Andrew Wilson · Eytan Bakshy

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1064

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.

Author Information

Max Balandat (Facebook)
Brian Karrer (Facebook)
Daniel Jiang (Facebook)
Samuel Daulton (Facebook)
Ben Letham (Facebook)
Andrew Wilson (New York University)

I am a professor of machine learning at New York University.

Eytan Bakshy (Facebook)

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