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
- 2. Val Tannen
- 3. Susan B. Davidson
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,867 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD | 0.00010272055 |
| 4,872 | Explainable AI: Foundations, Applications, Opportunities for Data Management Research | 2022 | SIGMOD | 5.8609352e-05 |
| 7,482 | Provenance-Enabled Explainable AI | 2024 | SIGMOD | 4.7180617e-05 |
| 7,796 | CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties | 2021 | VLDB | 4.6482625e-05 |
| 8,163 | Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science | 2021 | VLDB | 4.5723431e-05 |
| 8,853 | Complaint-Driven Training Data Debugging at Interactive Speeds | 2022 | SIGMOD | 4.4350727e-05 |
| 9,231 | Modyn: Data-Centric Machine Learning Pipeline Orchestration | 2025 | SIGMOD | 4.3690661e-05 |
| 10,213 | Stress-Testing Causal Claims via Cardinality Repairs | 2026 | SIGMOD | 4.1945683e-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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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,173 | Querying Data Provenance | 2010 | SIGMOD | 9.3676609e-05 |
| 6,186 | On Provenance Minimization | 2011 | PODS | 5.166082e-05 |
| 2,524 | Provenance Management in Curated Databases | 2006 | SIGMOD | 8.6017899e-05 |
| 4,383 | Incremental Record Linkage | 2014 | VLDB | 6.2383094e-05 |
| 11,471 | On Optimizing the Trade-off between Privacy and Utility in Data Provenance | 2021 | SIGMOD | 4.1945683e-05 |
| 6,879 | Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data | 2023 | SIGMOD | 4.8971368e-05 |
| 8,394 | Hypothetical Reasoning via Provenance Abstraction | 2019 | SIGMOD | 4.527807e-05 |
| 6,914 | Private Incremental Regression | 2017 | PODS | 4.8925595e-05 |
| 7,482 | Provenance-Enabled Explainable AI | 2024 | SIGMOD | 4.7180617e-05 |
| 8,163 | Capturing and Querying Fine-grained Provenance of Preprocessing Pipelines in Data Science | 2021 | VLDB | 4.5723431e-05 |