Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization

• URL: http://arxiv.org/abs/2107.14465v1
• Date: Fri, 30 Jul 2021 07:25:07 GMT
• Title: Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization
• Authors: Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low, Patrick Jaillet
• Abstract summary: This paper presents a novel trusted-maximizers entropy search (TES) acquisition function. It measures how much an input contributes to the information gain on a query over a finite set of trusted maximizers.
• Score: 39.824086260578646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function. However, they usually require several approximations or simplifying assumptions (without clearly understanding their effects on the BO performance) and/or their generalization to batch BO is computationally unwieldy, especially with an increasing batch size. To alleviate these issues, this paper presents a novel trusted-maximizers entropy search (TES) acquisition function: It measures how much an input query contributes to the information gain on the maximizer over a finite set of trusted maximizers, i.e., inputs optimizing functions that are sampled from the Gaussian process posterior belief of the objective function. Evaluating TES requires either only a stochastic approximation with sampling or a deterministic approximation with expectation propagation, both of which are investigated and empirically evaluated using synthetic benchmark objective functions and real-world optimization problems, e.g., hyperparameter tuning of a convolutional neural network and synthesizing 'physically realizable' faces to fool a black-box face recognition system. Though TES can naturally be generalized to a batch variant with either approximation, the latter is amenable to be scaled to a much larger batch size in our experiments.

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