Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916672370114560 |
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| author | Li, Wenxuan Wang, Taiyi Yoneki, Eiko |
| author_facet | Li, Wenxuan Wang, Taiyi Yoneki, Eiko |
| contents | Optimizing black-box functions in high-dimensional search spaces has been known to be challenging for traditional Bayesian Optimization (BO). In this paper, we introduce HiBO, a novel hierarchical algorithm integrating global-level search space partitioning information into the acquisition strategy of a local BO-based optimizer. HiBO employs a search-tree-based global-level navigator to adaptively split the search space into partitions with different sampling potential. The local optimizer then utilizes this global-level information to guide its acquisition strategy towards most promising regions within the search space. A comprehensive set of evaluations demonstrates that HiBO outperforms state-of-the-art methods in high-dimensional synthetic benchmarks and presents significant practical effectiveness in the real-world task of tuning configurations of database management systems (DBMSs). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23148 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach Li, Wenxuan Wang, Taiyi Yoneki, Eiko Machine Learning Optimizing black-box functions in high-dimensional search spaces has been known to be challenging for traditional Bayesian Optimization (BO). In this paper, we introduce HiBO, a novel hierarchical algorithm integrating global-level search space partitioning information into the acquisition strategy of a local BO-based optimizer. HiBO employs a search-tree-based global-level navigator to adaptively split the search space into partitions with different sampling potential. The local optimizer then utilizes this global-level information to guide its acquisition strategy towards most promising regions within the search space. A comprehensive set of evaluations demonstrates that HiBO outperforms state-of-the-art methods in high-dimensional synthetic benchmarks and presents significant practical effectiveness in the real-world task of tuning configurations of database management systems (DBMSs). |
| title | Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.23148 |