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 (University of Pennsylvania)
- 2. James Weimer (University of Pennsylvania)
- 3. Susan B. Davidson (University of Pennsylvania)
BibTeX Citation
@article{wu_vldb21,
title = {{CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties}},
author = {Wu, Yinjun and Weimer, James and Davidson, Susan B.},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2410--2418},
doi = {10.14778/3476249.3476290},
url = {https://doi.org/10.14778/3476249.3476290},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,324 | Outliers: The Good, the Bad and the Ugly | 2026 | SIGMOD | 5.093636e-05 |
| 11,211 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025235185 |
| 582 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00016148948 |
| 1,094 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00012214617 |
| 3,281 | Cleaning Crowdsourced Labels Using Oracles for Statistical Classification | 2019 | VLDB | 7.5706653e-05 |
| 4,130 | PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models | 2020 | SIGMOD | 6.8839621e-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 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,000 | Optimizing Inference Serving on Serverless Platforms | 2022 | VLDB |
| 2 | 5,518 | Self-supervised and Interpretable Data Cleaning with Sequence Generative Adversarial Networks | 2023 | VLDB |
| 3 | 10,805 | WeShap: Weak Supervision Source Evaluation with Shapley Values | 2025 | VLDB |
| 4 | 8,876 | LANCET: Labeling Complex Data at Scale | 2021 | VLDB |
| 5 | 6,816 | Inspector Gadget: A Data Programming-based Labeling System for Industrial Images | 2021 | VLDB |
| 6 | 11,211 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB |
| 7 | 11,746 | Active Reinforcement Learning for Data Preparation: Learn2Clean with Human-In-The-Loop | 2020 | CIDR |
| 8 | 1,094 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB |
| 9 | 10,800 | Two Birds with One Stone: Efficient Deep Learning over Mislabeled Data through Subset Selection | 2025 | SIGMOD |
| 10 | 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB |