Indirect Query Bayesian Optimization with Integrated Feedback

Autor:	Zhang, Mengyan, Bouabid, Shahine, Ong, Cheng Soon, Flaxman, Seth, Sejdinovic, Dino
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Preliminary work. Under review
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.13559 Zobrazit plný text záznamu View this record from Arxiv