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)
Incoming Non-self Citations Over Time
Authors
- 1. Jose Picado (Oregon State University)
- 2. Arash Termehchy (Oregon State University)
- 3. Alan Fern (Oregon State University)
- 4. Sudhanshu Pathak (Oregon State University)
- 5. Praveen Ilango (Oregon State University)
- 6. John Davis (Oregon State University)
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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