CUED Publications database

Predictive Entropy Search for Bayesian optimization with unknown constraints

Hernández-Lobato, JM and Gelbart, MA and Hoffman, MW and Adams, RP and Ghahramani, Z (2015) Predictive Entropy Search for Bayesian optimization with unknown constraints. 32nd International Conference on Machine Learning, ICML 2015, 2. pp. 1699-1707.

Full text not available from this repository.

Abstract

Copyright © 2015 by the author(s). Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement (EI) heuristic. However, EI can lead to pathologies when used with constraints. For example, in the case of decoupled constraints-i.e., when one can independently evaluate the objective or the constraints-EI can encounter a pathology that prevents exploration. Additionally, computing EI requires a current best solution, which may not exist if none of the data collected so far satisfy the constraints. By contrast, information-based approaches do not suffer from these failure modes. In this paper, we present a new information-based method called Predictive Entropy Search with Constraints (PESC). We analyze the performance of PESC and show that it compares favorably to El-based approaches on synthetic and benchmark problems, as well as several real-world examples. We demonstrate that PESC is an effective algorithm that provides a promising direction towards a unified solution for constrained Bayesian optimization.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:34
Last Modified: 09 Nov 2017 01:28
DOI: