Snuba: Automating Weak Supervision to Label Training Data
Summary: Snuba automates weak supervision by generating task-specific labeling heuristics from a small labeled set to label a large unlabeled corpus. It grows coverage iteratively with a statistical termination guarantee, finishing under five minutes and beating handcrafted rules by 9.74 F1 and semi-supervised baselines by 14.35 F1. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Paroma Varma (Stanford University)
- 2. Christopher Ré (Stanford University)
BibTeX Citation
@article{varma_vldb19,
title = {{Snuba: Automating Weak Supervision to Label Training Data}},
author = {Varma, Paroma and Ré, Christopher},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {3},
pages = {223--236},
doi = {10.14778/3291264.3291268},
url = {https://doi.org/10.14778/3291264.3291268},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 200 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025443474 |
| 422 | Big Data Integration | 2013 | VLDB | 0.00018451149 |
| 1,283 | Fusing Data with Correlations | 2014 | SIGMOD | 0.00011195703 |
| 2,942 | Fonduer: Knowledge Base Construction from Richly Formatted Data | 2018 | SIGMOD | 7.8171618e-05 |
| 3,797 | SLiMFast: Guaranteed Results for Data Fusion and Source Reliability | 2017 | SIGMOD | 6.9951279e-05 |
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