SnAKe: Bayesian Optimization with Pathwise Exploration

• URL: http://arxiv.org/abs/2202.00060v1
• Date: Mon, 31 Jan 2022 19:42:56 GMT
• Title: SnAKe: Bayesian Optimization with Pathwise Exploration
• Authors: Jose Pablo Folch, Shiqiang Zhang, Robert M Lee, Behrang Shafei, David Walz, Calvin Tsay, Mark van der Wilk, Ruth Misener
• Abstract summary: We consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. This paper investigates the problem and introduces 'Sequential Bayesian Optimization via Adaptive Connecting Samples' (SnAKe) It provides a solution by considering future queries and preemptively building optimization paths that minimize input costs.
• Score: 9.807656882149319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet microfluidic reactors, we consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. We further assume we are working asynchronously, meaning we have to decide on new queries before we finish evaluating previous experiments. This paper investigates the problem and introduces 'Sequential Bayesian Optimization via Adaptive Connecting Samples' (SnAKe), which provides a solution by considering future queries and preemptively building optimization paths that minimize input costs. We investigate some convergence properties and empirically show that the algorithm is able to achieve regret similar to classical Bayesian Optimization algorithms in both the synchronous and asynchronous settings, while reducing the input costs significantly.

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