Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples
Summary: Auto-FuzzyJoin auto-programs fuzzy similarity joins without labeled data by exploiting a geometric interpretation of distance-functions to meet a user-specified precision tau while maximizing recall. On 50 Wikipedia-derived fuzzy-join tasks, it beats unsupervised baselines and rivals supervised methods with partial labels; code and benchmark data are released on GitHub. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Peng Li (Georgia Institute of Technology)
- 2. Xiang Cheng (Georgia Institute of Technology)
- 3. Xu Chu (Georgia Institute of Technology)
- 4. Yeye He (Microsoft)
- 5. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@inproceedings{li_sigmod21,
title = {{Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples}},
author = {Li, Peng and Cheng, Xiang and Chu, Xu and He, Yeye and Chaudhuri, Surajit},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452824},
url = {https://dl.acm.org/doi/10.1145/3448016.3452824},
year = {2021}
}
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