Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction
Summary: Zero-shot cost models deliver out-of-the-box learned cost estimation that generalizes to unseen databases without training queries. A novel architecture and workload encoding enable transfer from pre-trained models, outperforming workload-driven baselines and supporting few-shot refinement on new databases. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Benjamin Hilprecht (Technical University of Darmstadt)
- 2. Carsten Binnig (German National Research Center for Information Technology; Technical University of Darmstadt)
BibTeX Citation
@article{hilprecht_vldb22,
title = {{Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction}},
author = {Hilprecht, Benjamin and Binnig, Carsten},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2361--2374},
doi = {10.14778/3551793.3551799},
url = {https://doi.org/10.14778/3551793.3551799},
year = {2022}
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