PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models
Summary: PrIU and PrIU-opt use data provenance to incrementally update regression models, avoiding full retraining. Correctness and convergence are proven, with experiments showing up to 100x speedups while preserving accuracy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yinjun Wu (University of Pennsylvania)
- 2. Val Tannen (University of Pennsylvania)
- 3. Susan B. Davidson (University of Pennsylvania)
BibTeX Citation
@inproceedings{wu_sigmod20,
title = {{PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models}},
author = {Wu, Yinjun and Tannen, Val and Davidson, Susan B.},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380571},
url = {https://dl.acm.org/doi/10.1145/3318464.3380571},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,951 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD | 9.4252389e-05 |
| 5,054 | Explainable AI: Foundations, Applications, Opportunities for Data Management Research | 2022 | SIGMOD | 6.3843089e-05 |
| 7,868 | CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties | 2021 | VLDB | 5.5277527e-05 |
| 7,907 | Provenance-Enabled Explainable AI | 2024 | SIGMOD | 5.5181056e-05 |
| 8,050 | Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science | 2021 | VLDB | 5.5000099e-05 |
| 8,859 | Complaint-Driven Training Data Debugging at Interactive Speeds | 2022 | SIGMOD | 5.356561e-05 |
| 8,949 | Modyn: Data-Centric Machine Learning Pipeline Orchestration | 2025 | SIGMOD | 5.3462965e-05 |
| 10,502 | Stress-Testing Causal Claims via Cardinality Repairs | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 5 | 11,668 | On Optimizing the Trade-off between Privacy and Utility in Data Provenance | 2021 | SIGMOD |
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| 8 | 7,517 | Private Incremental Regression | 2017 | PODS |
| 9 | 7,907 | Provenance-Enabled Explainable AI | 2024 | SIGMOD |
| 10 | 8,050 | Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science | 2021 | VLDB |