2024/12/18 by Mengyan Zhang, Shahine Bouabid, Zhang, Mengyan +7 · 1 citation
Computer Science · #Advanced Database Systems and Queries #Artificial intelligence #Bayesian Modeling and Causal Inference #Bayesian optimization #Bayesian probability #Computer science #FOS: Computer and information sciences #Information retrieval #Machine Learning (cs.LG) #Neural Networks and Applications #Query optimization
paper · pdf · doi:10.48550/arxiv.2412.13559
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 with multi-resolution feedback to improve computational efficiency. We show regret bounds for our proposed methods and demonstrate the effectiveness of our approaches on simulated optimization tasks.