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Poster
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its Parallelization
Shion Takeno · Hitoshi Fukuoka · Yuhki Tsukada · Toshiyuki Koyama · Motoki Shiga · Ichiro Takeuchi · Masayuki Karasuyama

Tue Jul 14 06:00 PM -- 06:45 PM & Wed Jul 15 04:00 AM -- 04:45 AM (PDT) @
in Poster Session 10 »

In a standard setting of Bayesian optimization (BO), the objective function evaluation is assumed to be highly expensive. Multi-fidelity Bayesian optimization (MFBO) accelerates BO by incorporating lower fidelity observations available with a lower sampling cost. We propose a novel information-theoretic approach to MFBO, called multi-fidelity max-value entropy search (MF-MES), that enables us to obtain a more reliable evaluation of the information gain compared with existing information-based methods for MFBO. Further, we also propose a parallelization of MF-MES mainly for the asynchronous setting because queries typically occur asynchronously in MFBO due to a variety of sampling costs. We show that most of computations in our acquisition functions can be derived analytically, except for at most only two dimensional numerical integration that can be performed efficiently by simple approximations. We demonstrate effectiveness of our approach by using benchmark datasets and a real-world application to materials science data.

Keywords: Bayesian Methods · Gaussian Processes · Non-convex Optimization · Probabilistic Inference - Models and Probabilistic Programming

Author Information

Shion Takeno (Nagoya Institute of Technology)
Hitoshi Fukuoka (Hitachi Metals, Ltd.)
Yuhki Tsukada (Nagoya University)
Toshiyuki Koyama (Nagoya University)
Motoki Shiga (Gifu University)
Ichiro Takeuchi (Nagoya Institute of Technology / RIKEN)
Masayuki Karasuyama (Nagoya Institute of Technology)

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