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 |
|---|---|---|---|---|
| 11,218 | WeShap: Weak Supervision Source Evaluation with Shapley Values | 2025 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
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 | 0.00025181304 |
| 1,120 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00011946047 |
| 3,997 | Overton: A Data System for Monitoring and Improving Machine-Learned Products | 2020 | CIDR | 6.8655222e-05 |
| 4,543 | GOGGLES: Automatic Image Labeling with Affinity Coding | 2020 | SIGMOD | 6.5470116e-05 |
| 5,181 | Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale | 2019 | SIGMOD | 6.2406557e-05 |
| 5,850 | Adaptive Rule Discovery for Labeling Text Data | 2021 | SIGMOD | 5.9719174e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,756 | Exploratory Training: When Annotators Learn About Data | 2023 | SIGMOD |
| 2 | 11,407 | mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines” | 2025 | VLDB |
| 3 | 6,937 | Inspector Gadget: A Data Programming-based Labeling System for Industrial Images | 2021 | VLDB |
| 4 | 975 | Democratizing Data Science through Interactive Curation of ML Pipelines | 2019 | SIGMOD |
| 5 | 6,165 | Demonstration of Panda: A Weakly Supervised Entity Matching System | 2021 | VLDB |
| 6 | 7,473 | Witan: Unsupervised Labelling Function Generation for Assisted Data Programming | 2022 | VLDB |
| 7 | 3,627 | The Role of Massively Multi-Task and Weak Supervision in Software 2.0 | 2019 | CIDR |
| 8 | 1,120 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB |
| 9 | 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB |
| 10 | 11,218 | WeShap: Weak Supervision Source Evaluation with Shapley Values | 2025 | VLDB |