Efficiently Mitigating the Impact of Data Drift on Machine Learning Pipelines
Summary: Introduces Data Distributions with Low Accuracy (DDLA), using decision trees to localize serving-data regions where drift harms black-box model accuracy. Enables selective retraining only for harmful drift, reducing pipeline costs while preserving predictive quality. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Sijie Dong (Université Paris Cité)
- 2. Qitong Wang (Université Paris Cité)
- 3. Soror Sahri (Université Paris Cité)
- 4. Themis Palpanas (Université Paris Cité)
- 5. Divesh Srivastava (AT&T)
BibTeX Citation
@article{dong_vldb24,
title = {{Efficiently Mitigating the Impact of Data Drift on Machine Learning Pipelines}},
author = {Dong, Sijie and Wang, Qitong and Sahri, Soror and Palpanas, Themis and Srivastava, Divesh},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3072--3081},
doi = {10.14778/3681954.3681984},
url = {https://doi.org/10.14778/3681954.3681984},
year = {2024}
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Outgoing Citations (Sorted by Pagerank)
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 |
|---|---|---|---|---|
| 582 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00016148948 |
| 858 | Interpretable and Informative Explanations of Outcomes | 2015 | VLDB | 0.0001356511 |
| 1,350 | Automating Large-Scale Data Quality Verification | 2018 | VLDB | 0.00011065626 |
| 2,147 | Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions | 2021 | VLDB | 9.0831495e-05 |
| 2,657 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD | 8.2887895e-05 |
| 3,670 | Learning to Validate the Predictions of Black Box Classifiers on Unseen Data | 2020 | SIGMOD | 7.2118928e-05 |
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