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Poster
Bayesian Optimization for Distributionally Robust Chance-constrained Problem
Yu Inatsu · Shion Takeno · Masayuki Karasuyama · Ichiro Takeuchi

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #731
in Poster Session 1 »

In black-box function optimization, we need to consider not only controllable design variables but also uncontrollable stochastic environment variables. In such cases, it is necessary to solve the optimization problem by taking into account the uncertainty of the environmental variables. Chance-constrained (CC) problem, the problem of maximizing the expected value under a certain level of constraint satisfaction probability, is one of the practically important problems in the presence of environmental variables. In this study, we consider distributionally robust CC (DRCC) problem and propose a novel DRCC Bayesian optimization method for the case where the distribution of the environmental variables cannot be precisely specified. We show that the proposed method can find an arbitrary accurate solution with high probability in a finite number of trials, and confirm the usefulness of the proposed method through numerical experiments.

Keywords: OPT: Optimization and Learning under Uncertainty · OPT: Zero-order and Black-box Optimization · PM: Gaussian Processes · T: Active Learning and Interactive Learning

Author Information

Yu Inatsu (Nagoya Institute of Technology)
Shion Takeno (Nagoya Institute of Technology)
Masayuki Karasuyama (Nagoya Institute of Technology)
Ichiro Takeuchi (Nagoya Institute of Technology / RIKEN)

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