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Conformance Constraint Discovery: Measuring Trust in Data-Driven Systems

Summary: Conformance constraints: a data-profiling primitive to quantify non-conformance between serving data and training assumptions. Low-variance projections yield strong constraints; linear-in-data, cubic-in-attributes discovery with a quantitative non-conformance score supports trusted ML and drift detection. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6138
Venue
SIGMOD
Year
2021
Pagerank
5.6256632e-05
Overall Rank
7,397 | 49.26%
DOI
10.1145/3448016.3452795

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{fariha_sigmod21,
        title = {{Conformance Constraint Discovery: Measuring Trust in Data-Driven Systems}},
        author = {Fariha, Anna and Tiwari, Ashish and Radhakrishna, Arjun and Gulwani, Sumit and Meliou, Alexandra},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452795},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452795},
        year = {2021}
}

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