Poster

Multi-objective Bayesian Optimization using Pareto-frontier Entropy

Shinya Suzuki · Shion Takeno · Tomoyuki Tamura · Kazuki Shitara · Masayuki Karasuyama

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

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Abstract:

This paper studies an entropy-based multi-objective Bayesian optimization (MBO). Existing entropy-based MBO methods need complicated approximations to evaluate entropy or employ over-simplification that ignores trade-off among objectives. We propose a novel entropy-based MBO called Pareto-frontier entropy search (PFES), which is based on the information gain of Pareto-frontier. We show that our entropy evaluation can be reduced to a closed form whose computation is quite simple while capturing the trade-off relation in Pareto-frontier. We further propose an extension for the ``decoupled'' setting, in which each objective function can be observed separately, and show that the PFES-based approach derives a natural extension of the original acquisition function which can also be evaluated simply. Our numerical experiments show effectiveness of PFES through several benchmark datasets, and real-word datasets from materials science.

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