Stable Bayesian Optimisation via Direct Stability Quantification
Abstract
In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions. We are motivated by the optimisation of physical and industrial processes where, for some input ranges, small and unavoidable variations in inputs lead to unacceptably large variation in outputs. Our approach uses multiple gradient Gaussian Process models to estimate the probability that worst-case output variation for specified input perturbation exceeded the desired maxima, and these probabilities are then used to (a) guide the optimisation process toward solutions satisfying our stability criteria and (b) post-filter results to find the best stable solution. We exhibit our algorithm on synthetic and real-world problems and demonstrate that it is able to effectively find stable maxima.
Cite
@article{arxiv.1902.07846,
title = {Stable Bayesian Optimisation via Direct Stability Quantification},
author = {Alistair Shilton and Sunil Gupta and Santu Rana and Svetha Venkatesh and Majid Abdolshah and Dang Nguyen},
journal= {arXiv preprint arXiv:1902.07846},
year = {2019}
}