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 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 582 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00016148948 |
| 2,147 | Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions | 2021 | VLDB | 9.0831495e-05 |
| 3,886 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD | 7.0460597e-05 |
| 4,835 | Adaptive Data Augmentation for Supervised Learning over Missing Data | 2021 | VLDB | 6.486592e-05 |
| 7,880 | Learning Over Dirty Data Without Cleaning | 2020 | SIGMOD | 5.5244204e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,896 | Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation | 2025 | VLDB |
| 2 | 2,208 | Query Optimization for Dynamic Imputation | 2017 | VLDB |
| 3 | 3,886 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD |
| 4 | 9,993 | In-Database Data Imputation | 2024 | SIGMOD |
| 5 | 11,258 | Win-Win: On Simultaneous Clustering and Imputing over Incomplete Data | 2024 | VLDB |
| 6 | 2,147 | Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions | 2021 | VLDB |
| 7 | 8,002 | Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints | 2020 | SIGMOD |
| 8 | 4,034 | Missing Value Imputation on Multidimensional Time Series | 2021 | VLDB |
| 9 | 3,386 | Efficient and Effective Data Imputation with Influence Functions | 2022 | VLDB |
| 10 | 6,642 | Missing Data Imputation with Uncertainty-Driven Network | 2024 | SIGMOD |