Failure-Aware Gaussian Process Optimization with Regret Bounds

被引:0
作者
Iwazaki, Shogo [1 ]
Takeno, Shion [2 ]
Tanabe, Tomohiko [1 ]
Irie, Mitsuru [1 ]
机构
[1] MI 6 Ltd, Tokyo, Japan
[2] RIKEN AIP, Tokyo, Japan
来源
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023) | 2023年
关键词
BAYESIAN OPTIMIZATION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
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页数:13
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