Summary of Hibo: Hierarchical Bayesian Optimization Via Adaptive Search Space Partitioning, by Wenxuan Li et al.
HiBO: Hierarchical Bayesian Optimization via Adaptive Search Space Partitioning
by Wenxuan Li, Taiyi Wang, Eiko Yoneki
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary HiBO, a novel hierarchical algorithm, is introduced to optimize black-box functions in high-dimensional search spaces. Traditional Bayesian Optimization (BO) faces challenges in these scenarios. HiBO integrates global-level search space partitioning information into the acquisition strategy of a local BO-based optimizer. The algorithm employs a search-tree-based global-level navigator to split the search space into partitions with different sampling potential. The local optimizer then uses this information to guide its acquisition strategy towards promising regions. Evaluations demonstrate that HiBO outperforms state-of-the-art methods in high-dimensional synthetic benchmarks and shows practical effectiveness in tuning database management systems configurations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HiBO is a new way to find the best solution for complex problems. Traditional methods have trouble finding the right answer when there are many possibilities. HiBO helps by breaking down the search space into smaller parts, like a map. This gives the algorithm a better idea of where to look next. The results show that HiBO is better than other methods at solving these kinds of problems. |
Keywords
* Artificial intelligence * Optimization
