Adversarial Query Synthesis via Bayesian Optimization
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910037751889920 |
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| author | Tao, Jeffrey Zeng, Yimeng Jones, Haydn Thomas Maus, Natalie Bastani, Osbert Gardner, Jacob R. Marcus, Ryan |
| author_facet | Tao, Jeffrey Zeng, Yimeng Jones, Haydn Thomas Maus, Natalie Bastani, Osbert Gardner, Jacob R. Marcus, Ryan |
| contents | Benchmark workloads are extremely important to the database management research community, especially as more machine learning components are integrated into database systems. Here, we propose a Bayesian optimization technique to automatically search for difficult benchmark queries, significantly reducing the amount of manual effort usually required. In preliminary experiments, we show that our approach can generate queries with more than double the optimization headroom compared to existing benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01570 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adversarial Query Synthesis via Bayesian Optimization Tao, Jeffrey Zeng, Yimeng Jones, Haydn Thomas Maus, Natalie Bastani, Osbert Gardner, Jacob R. Marcus, Ryan Databases Machine Learning Benchmark workloads are extremely important to the database management research community, especially as more machine learning components are integrated into database systems. Here, we propose a Bayesian optimization technique to automatically search for difficult benchmark queries, significantly reducing the amount of manual effort usually required. In preliminary experiments, we show that our approach can generate queries with more than double the optimization headroom compared to existing benchmarks. |
| title | Adversarial Query Synthesis via Bayesian Optimization |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2603.01570 |