A Statistical Perspective on Discovering Functional Dependencies in Noisy Data
Summary: FD discovery under noise as structure learning on binary variables; variables are data functions. FDX: sparse-regression framework turning FD discovery into regression; robust to noise/missing data, scalable to large datasets with ~2x F1 gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yunjia Zhang (University of Wisconsin)
- 2. Zhihan Guo (University of Wisconsin)
- 3. Theodoros Rekatsinas (University of Wisconsin)
BibTeX Citation
@inproceedings{zhang_sigmod20,
title = {{A Statistical Perspective on Discovering Functional Dependencies in Noisy Data}},
author = {Zhang, Yunjia and Guo, Zhihan and Rekatsinas, Theodoros},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389749},
url = {https://dl.acm.org/doi/10.1145/3318464.3389749},
year = {2020}
}
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