WeShap: Weak Supervision Source Evaluation with Shapley Values
Summary: WeShap: a Shapley-value metric quantifying each weak supervision source's average contribution in a proxy PWS pipeline. Efficient quadratic-time DP for exact computation; empirically generalizes across PWS variants and guides pipeline edits that boost downstream accuracy by ≈5 points. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Naiqing Guan
- 2. Nick Koudas
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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 |
|---|---|---|---|---|
| 252 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00030532082 |
| 1,218 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.00013221309 |
| 1,293 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012749902 |
| 5,257 | Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale | 2019 | SIGMOD | 5.5975788e-05 |
| 7,284 | Witan: Unsupervised Labelling Function Generation for Assisted Data Programming | 2022 | VLDB | 4.7716465e-05 |
| 8,285 | Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming | 2022 | VLDB | 4.5392079e-05 |
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