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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)
- Paper ID
- 11942
- Venue
- VLDB
- Year
- 2019
- Pagerank
- 0.00013221309
- Overall Rank
- 1,218 | 91.54%
- DOI
-
10.14778/3291264.3291268
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 26 of 26 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 1,088 |
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes |
2024 |
VLDB |
0.00014158762 |
| 2,831 |
Smile: A System to Support Machine Learning on EEG Data at Scale |
2019 |
VLDB |
8.0485807e-05 |
| 2,960 |
The Role of Massively Multi-Task and Weak Supervision in Software 2.0 |
2019 |
CIDR |
7.8103118e-05 |
| 4,471 |
GOGGLES: Automatic Image Labeling with Affinity Coding |
2020 |
SIGMOD |
6.1496765e-05 |
| 4,875 |
Explainable AI: Foundations, Applications, Opportunities for Data Management Research |
2022 |
SIGMOD |
5.8552996e-05 |
| 5,244 |
Towards Benchmarking Feature Type Inference for AutoML Platforms |
2021 |
SIGMOD |
5.6021738e-05 |
| 5,358 |
Adaptive Rule Discovery for Labeling Text Data |
2021 |
SIGMOD |
5.5507062e-05 |
| 5,974 |
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and Beyond |
2021 |
SIGMOD |
5.2458154e-05 |
| 6,122 |
VOCAL: Video Organization and Interactive Compositional AnaLytics |
2022 |
CIDR |
5.1966758e-05 |
| 6,953 |
Inspector Gadget: A Data Programming-based Labeling System for Industrial Images |
2021 |
VLDB |
4.8817419e-05 |
| 7,284 |
Witan: Unsupervised Labelling Function Generation for Assisted Data Programming |
2022 |
VLDB |
4.7716465e-05 |
| 7,798 |
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties |
2021 |
VLDB |
4.6438053e-05 |
| 8,285 |
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming |
2022 |
VLDB |
4.5392079e-05 |
| 8,337 |
CrowdGame: A Game-Based Crowdsourcing System for Cost-Effective Data Labeling |
2019 |
SIGMOD |
4.5385651e-05 |
| 8,515 |
UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads |
2022 |
VLDB |
4.4901466e-05 |
| 8,588 |
Exploratory Training: When Annotators Learn About Data |
2023 |
SIGMOD |
4.4853244e-05 |
| 8,713 |
LANCET: Labeling Complex Data at Scale |
2021 |
VLDB |
4.4577046e-05 |
| 9,415 |
Ground Truth Inference for Weakly Supervised Entity Matching |
2023 |
SIGMOD |
4.3399748e-05 |
| 9,776 |
Data Augmentation for ML-driven Data Preparation and Integration |
2021 |
VLDB |
4.2815042e-05 |
| 9,787 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
4.2799988e-05 |
| 9,872 |
CORAL: Collaborative Automatic Labeling System based on Large Language Models |
2024 |
VLDB |
4.2626861e-05 |
| 10,303 |
Morphing-based Compression for Data-centric ML Pipelines |
2026 |
VLDB |
4.1905499e-05 |
| 10,475 |
A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplaces |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,542 |
WeShap: Weak Supervision Source Evaluation with Shapley Values |
2025 |
VLDB |
4.1905499e-05 |
| 11,207 |
Steered Training Data Generation for Learned Semantic Type Detection |
2023 |
SIGMOD |
4.1905499e-05 |
| 11,633 |
Leveraging Organizational Resources to Adapt Models to New Data Modalities |
2020 |
VLDB |
4.1905499e-05 |
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.
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