Failure-Aware Gaussian Process Optimization with Regret Bounds
Shogo Iwazaki, Shion Takeno, Tomohiko Tanabe, Mitsuru Irie
Abstract
Real-world optimization problems often require black-box optimization with observation failure, where we can obtain the objective function value if we succeed, otherwise, we can only obtain a fact of failure. Moreover, this failure region can be complex by several latent constraints, whose number is also unknown. For this problem, we propose a failure-aware Gaussian process upper confidence bound (F-GP-UCB), which only requires a mild assumption for the observation failure that an optimal solution lies on an interior of a feasible region. Furthermore, we show that the number of successful observations grows linearly, by which we provide the first regret upper bounds and the convergence of F-GP-UCB. We demonstrate the effectiveness of F-GP-UCB in several benchmark functions, including the simulation function motivated by material synthesis experiments.
BibTeX
@inproceedings{
iwazaki2023failureaware,
title={Failure-Aware Gaussian Process Optimization with Regret Bounds},
author={Shogo Iwazaki and Shion Takeno and Tomohiko Tanabe and Mitsuru Irie},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=H5pwAeYAun}
}