Multi-Step Bayesian Optimization for One-Dimensional Feasibility Determination
Optimization and Control
2016-07-13 v1 Machine Learning
Computation
Abstract
Bayesian optimization methods allocate limited sampling budgets to maximize expensive-to-evaluate functions. One-step-lookahead policies are often used, but computing optimal multi-step-lookahead policies remains a challenge. We consider a specialized Bayesian optimization problem: finding the superlevel set of an expensive one-dimensional function, with a Markov process prior. We compute the Bayes-optimal sampling policy efficiently, and characterize the suboptimality of one-step lookahead. Our numerical experiments demonstrate that the one-step lookahead policy is close to optimal in this problem, performing within 98% of optimal in the experimental settings considered.
Cite
@article{arxiv.1607.03195,
title = {Multi-Step Bayesian Optimization for One-Dimensional Feasibility Determination},
author = {J. Massey Cashore and Lemuel Kumarga and Peter I. Frazier},
journal= {arXiv preprint arXiv:1607.03195},
year = {2016}
}