Learned Offline Query Planning via Bayesian Optimization
Summary: Offline query planning for repeated analytics workloads; learned exploration. Variational auto-encoders + Bayesian optimization search broad plan space, using execution as feedback; outperforms PostgreSQL-optimal and RL baselines on several datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jeffrey Tao (University of Pennsylvania)
- 2. Natalie Maus (University of Pennsylvania)
- 3. Haydn Jones (University of Pennsylvania)
- 4. Yimeng Zeng (University of Pennsylvania)
- 5. Jacob R. Gardner (University of Pennsylvania)
- 6. Ryan Marcus (University of Pennsylvania)
BibTeX Citation
@inproceedings{tao_sigmod25,
title = {{Learned Offline Query Planning via Bayesian Optimization}},
author = {Tao, Jeffrey and Maus, Natalie and Jones, Haydn and Zeng, Yimeng and Gardner, Jacob R. and Marcus, Ryan},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725316},
url = {https://dl.acm.org/doi/10.1145/3725316},
year = {2025}
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