Snorkel: Rapid Training Data Creation with Weak Supervision
Summary: Snorkel enables rapid ML training from weak supervision via labeling functions with unknown accuracies. End-to-end data programming denoises labels without ground truth, with a tradeoff optimizer, showing speedups and accuracy gains over hand labeling. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alexander Ratner (Stanford University)
- 2. Stephen H. Bach (Stanford University)
- 3. Henry Ehrenberg (Stanford University)
- 4. Jason Fries (Stanford University)
- 5. Sen Wu (Stanford University)
- 6. Christopher Ré (Stanford University)
BibTeX Citation
@article{ratner_vldb18,
title = {{Snorkel: Rapid Training Data Creation with Weak Supervision}},
author = {Ratner, Alexander and Bach, Stephen H. and Ehrenberg, Henry and Fries, Jason and Wu, Sen and Ré, Christopher},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {3},
pages = {269--282},
doi = {10.14778/3157794.3157797},
url = {https://doi.org/10.14778/3157794.3157797},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 24 of 74 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 100 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00033951027 |
| 422 | Big Data Integration | 2013 | VLDB | 0.00018451149 |
| 507 | A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration | 2012 | VLDB | 0.00017144751 |
| 1,283 | Fusing Data with Correlations | 2014 | SIGMOD | 0.00011195703 |
| 3,797 | SLiMFast: Guaranteed Results for Data Fusion and Source Reliability | 2017 | SIGMOD | 6.9951279e-05 |
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