CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties
Summary: CHEF cuts label-cleaning costs under weak supervision by prioritizing influential samples and feeding cleaned labels. It adds Increm-INFL and DeltaGrad-L, incremental selection and model updates, plus compact small-batch iteration enabling early stopping. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yinjun Wu
- 2. James Weimer
- 3. Susan B. Davidson
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,029 | Outliers: The Good, the Bad and the Ugly | 2026 | SIGMOD | 4.1905499e-05 |
| 11,003 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 252 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00030532082 |
| 788 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00016618698 |
| 1,218 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00013221309 |
| 3,767 | Cleaning Crowdsourced Labels Using Oracles for Statistical Classification | 2019 | VLDB | 6.7748725e-05 |
| 4,423 | PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models | 2020 | SIGMOD | 6.1925724e-05 |
| 4,471 | GOGGLES: Automatic Image Labeling with Affinity Coding | 2020 | SIGMOD | 6.1496765e-05 |
| 5,257 | Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale | 2019 | SIGMOD | 5.5975788e-05 |
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