Indirect Query Bayesian Optimization with Integrated Feedback

• URL: http://arxiv.org/abs/2412.13559v1
• Date: Wed, 18 Dec 2024 07:20:33 GMT
• Title: Indirect Query Bayesian Optimization with Integrated Feedback
• Authors: Mengyan Zhang, Shahine Bouabid, Cheng Soon Ong, Seth Flaxman, Dino Sejdinovic,
• Abstract summary: We develop a new class of Bayesian optimization problems where integrated feedback is given via a conditional expectation of the unknown function $f$ to be optimized. The goal is to find the global optimum of $f$ by adaptively querying and observing in the space transformed by the conditional distribution. This is motivated by real-world applications where one cannot access direct feedback due to privacy, hardware or computational constraints.
• Score: 17.66813850517961
• License:
• Abstract: We develop the framework of Indirect Query Bayesian Optimization (IQBO), a new class of Bayesian optimization problems where the integrated feedback is given via a conditional expectation of the unknown function $f$ to be optimized. The underlying conditional distribution can be unknown and learned from data. The goal is to find the global optimum of $f$ by adaptively querying and observing in the space transformed by the conditional distribution. This is motivated by real-world applications where one cannot access direct feedback due to privacy, hardware or computational constraints. We propose the Conditional Max-Value Entropy Search (CMES) acquisition function to address this novel setting, and propose a hierarchical search algorithm to address the multi-resolution setting and improve the computational efficiency. We show regret bounds for our proposed methods and demonstrate the effectiveness of our approaches on simulated optimization tasks.

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