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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Cheng Zhen (Oregon State University)
- 2. Nischal Aryal (Oregon State University)
- 3. Arash Termehchy (Oregon State University)
- 4. Amandeep Singh Chabada (Oregon State University)
BibTeX Citation
@inproceedings{zhen_sigmod24,
title = {{Certain and Approximately Certain Models for Statistical Learning}},
author = {Zhen, Cheng and Aryal, Nischal and Termehchy, Arash and Chabada, Amandeep Singh},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654929},
url = {https://dl.acm.org/doi/10.1145/3654929},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,975 | Minimal Data Cleaning for Model Training by MinPrep | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 483 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00017590977 |
| 1,847 | Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions | 2021 | VLDB | 9.5120573e-05 |
| 3,941 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD | 6.9138042e-05 |
| 4,953 | Adaptive Data Augmentation for Supervised Learning over Missing Data | 2021 | VLDB | 6.3416889e-05 |
| 7,875 | Learning Over Dirty Data Without Cleaning | 2020 | SIGMOD | 5.4355826e-05 |
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