WeShap: Weak Supervision Source Evaluation with Shapley Values
Summary: WeShap applies Shapley values to quantify weak-supervision source contributions, enabling diagnosis of helpful, harmful, and mislabeled outputs. A dynamic-programming algorithm computes scores in quadratic time and improves downstream accuracy by 5 points through pipeline revision. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Naiqing Guan (University of Toronto)
- 2. Nick Koudas (University of Toronto)
BibTeX Citation
@article{guan_vldb25,
title = {{WeShap: Weak Supervision Source Evaluation with Shapley Values}},
author = {Guan, Naiqing and Koudas, Nick},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {4},
pages = {1063--1076},
doi = {10.14778/3717755.3717766},
url = {https://doi.org/10.14778/3717755.3717766},
year = {2025}
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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 |
| 1,066 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012333161 |
| 1,094 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00012214617 |
| 5,058 | Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale | 2019 | SIGMOD | 6.3815523e-05 |
| 7,331 | Witan: Unsupervised Labelling Function Generation for Assisted Data Programming | 2022 | VLDB | 5.6435171e-05 |
| 8,523 | Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming | 2022 | VLDB | 5.4119882e-05 |
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