Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
Summary: Nemo is an interactive weak-supervision system that formalizes heuristic design as development over a chosen data subset. It optimizes development-data selection and uses context to improve heuristics, boosting WS productivity ~20% (up to 47%). (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Cheng-Yu Hsieh (University of Washington)
- 2. Jieyu Zhang (University of Washington)
- 3. Alexander Ratner (Snorkel AI, Inc.; University of Washington)
BibTeX Citation
@article{hsieh_vldb22,
title = {{Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming}},
author = {Hsieh, Cheng-Yu and Zhang, Jieyu and Ratner, Alexander},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {13},
pages = {4093--4105},
doi = {10.14778/3565838.3565859},
url = {https://doi.org/10.14778/3565838.3565859},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,805 | WeShap: Weak Supervision Source Evaluation with Shapley Values | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025235185 |
| 1,094 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00012214617 |
| 3,947 | Overton: A Data System for Monitoring and Improving Machine-Learned Products | 2020 | CIDR | 7.0040437e-05 |
| 4,451 | GOGGLES: Automatic Image Labeling with Affinity Coding | 2020 | SIGMOD | 6.6952549e-05 |
| 5,058 | Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale | 2019 | SIGMOD | 6.3815523e-05 |
| 5,722 | Adaptive Rule Discovery for Labeling Text Data | 2021 | SIGMOD | 6.1089867e-05 |
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