Indirect Query Bayesian Optimization with Integrated Feedback
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915444574650368 |
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| author | Zhang, Mengyan Bouabid, Shahine Ong, Cheng Soon Flaxman, Seth Sejdinovic, Dino |
| author_facet | Zhang, Mengyan Bouabid, Shahine Ong, Cheng Soon Flaxman, Seth Sejdinovic, Dino |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13559 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Indirect Query Bayesian Optimization with Integrated Feedback Zhang, Mengyan Bouabid, Shahine Ong, Cheng Soon Flaxman, Seth Sejdinovic, Dino Machine Learning 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. |
| title | Indirect Query Bayesian Optimization with Integrated Feedback |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.13559 |