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
- 2. Jieyu Zhang
- 3. Alexander Ratner
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,533 | WeShap: Weak Supervision Source Evaluation with Shapley Values | 2025 | VLDB | 4.1945683e-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 |
|---|---|---|---|---|
| 254 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00030540555 |
| 1,215 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.0001323375 |
| 4,196 | Overton: A Data System for Monitoring and Improving Machine-Learned Products | 2020 | CIDR | 6.3686231e-05 |
| 4,471 | GOGGLES: Automatic Image Labeling with Affinity Coding | 2020 | SIGMOD | 6.1555681e-05 |
| 5,251 | Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale | 2019 | SIGMOD | 5.6029615e-05 |
| 5,347 | Adaptive Rule Discovery for Labeling Text Data | 2021 | SIGMOD | 5.5560452e-05 |
Previous
Page 1 / 1
Next