We consider derivative-free black-box global optimization of expensive noisy functions, when most of the randomness in the objective is produced by a few influential scalar random inputs. We present a new Bayesian global optimization algorithm, called Stratified Bayesian Optimization (SBO), which uses this strong dependence to improve performance. Our algorithm is similar in spirit to stratification, a technique from simulation, which uses strong dependence on a categorical representation of the random input to reduce variance. We demonstrate in numerical experiments that SBO outperforms state-of-the-art Bayesian optimization benchmarks that do not leverage this dependence.
@article{arxiv.1602.02338,
title = {Stratified Bayesian Optimization},
author = {Saul Toscano-Palmerin and Peter I. Frazier},
journal= {arXiv preprint arXiv:1602.02338},
year = {2016}
}