Pessimistic asynchronous sampling in high-cost Bayesian optimization

TL;DR

This paper introduces a pessimistic asynchronous sampling approach in Bayesian optimization that accelerates the search for optimal experimental conditions, especially in high-cost spaces, by evaluating new policies in simulation.

Contribution

It extends asynchronous Bayesian optimization by evaluating five policies with pessimistic predictions, demonstrating improved efficiency and robustness over traditional methods.

Findings

Pessimistic policies often find optima with fewer experiments.

Pessimistic approach reduces susceptibility to local optima.

Asynchronous methods outperform serial sampling in high-dimensional spaces.

Abstract

Asynchronous Bayesian optimization is a recently implemented technique that allows for parallel operation of experimental systems and disjointed workflows. Contrasting with serial Bayesian optimization which individually selects experiments one at a time after conducting a measurement for each experiment, asynchronous policies sequentially assign multiple experiments before measurements can be taken and evaluate new measurements continuously as they are made available. This technique allows for faster data generation and therefore faster optimization of an experimental space. This work extends the capabilities of asynchronous optimization methods beyond prior studies by evaluating four additional policies that incorporate pessimistic predictions in the training data set. Combined with a conventional policy that uses model predictions, the five total policies were evaluated in a simulated environment and benchmarked with serial sampling. Under some conditions and parameter space dimensionalities, the pessimistic prediction asynchronous policy reached optimum experimental conditions in significantly fewer experiments than equivalent serial policies and proved to be less susceptible to convergence onto local optima at higher dimensions. Without accounting for the faster sampling rate, the pessimistic asynchronous algorithm presented in this work could result in more efficient algorithm driven optimization of high-cost experimental spaces. Accounting for sampling rate, the presented asynchronous algorithm could allow for faster optimization in experimental spaces where multiple experiments can be run before results are collected.