Minimal Data Cleaning for Model Training by MinPrep
Summary: MinPrep determines whether dirty training data must be repaired to meet a user-specified accuracy target, otherwise training directly on clean data. If needed, it repairs only a provably minimal subset, supporting convex and SGD-trained nonconvex models. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Cheng Zhen (Oregon State University)
- 2. Prayoga (Oregon State University)
- 3. Nischal Aryal (Oregon State University)
- 4. Arash Termehchy (Oregon State University)
- 5. Alireza Aghasi (Oregon State University)
BibTeX Citation
@article{zhen_vldb26,
title = {{Minimal Data Cleaning for Model Training by MinPrep}},
author = {Zhen, Cheng and Prayoga and Aryal, Nischal and Termehchy, Arash and Aghasi, Alireza},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4618--4621},
doi = {10.14778/3827998.3828080},
url = {https://doi.org/10.14778/3827998.3828080},
year = {2026}
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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 |
|---|---|---|---|---|
| 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 |
| 5,071 | Data Collection and Quality Challenges for Deep Learning | 2020 | VLDB | 6.2879908e-05 |
| 11,514 | Certain and Approximately Certain Models for Statistical Learning | 2024 | SIGMOD | 4.9793485e-05 |
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