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Scalable and Usable Relational Learning With Automatic Language Bias

Summary: AutoBias automatically induces data-driven language bias to guide relational model learning, reducing manual bias engineering. Efficient sampling and learning scale to large datasets, achieving comparable accuracy to manual bias with modest overhead. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6228
Venue
SIGMOD
Year
2021
Pagerank
5.1708123e-05
Overall Rank
10,042 | 31.11%
DOI
10.1145/3448016.3457275

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{picado_sigmod21,
        title = {{Scalable and Usable Relational Learning With Automatic Language Bias}},
        author = {Picado, Jose and Termehchy, Arash and Fern, Alan and Pathak, Sudhanshu and Ilango, Praveen and Davis, John},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457275},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457275},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,466 InferF: Declarative Factorization of AI/ML Inferences over Joins 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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