Certain and Approximately Certain Models for Statistical Learning
Summary: Unified framework for deciding when imputation is unnecessary to train accurate statistical models on incomplete data. Efficient, theory-backed algorithms certify certain/approximately certain learning across common ML paradigms, often avoiding costly imputation with little overhead. (summarized by gpt-5.4-mini on May 24 2026)
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Authors
- 1. Cheng Zhen
- 2. Nischal Aryal
- 3. Arash Termehchy
- 4. Amandeep Singh Chabada
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 791 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00016629664 |
| 2,302 | Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions | 2021 | VLDB | 9.0668832e-05 |
| 4,102 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD | 6.4522929e-05 |
| 5,028 | Adaptive Data Augmentation for Supervised Learning over Missing Data | 2021 | VLDB | 5.7506746e-05 |
| 7,867 | Learning Over Dirty Data Without Cleaning | 2020 | SIGMOD | 4.6320452e-05 |
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