Zorro: Quantifying Uncertainty in Models & Predictions Arising from Dirty Data
Summary: Zorro quantifies uncertainty from dirty data by considering all plausible clean datasets and their linear models. It over-approximates possible models and predictions to certify robustness of parameters and predictions against data quality issues. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kaiyuan Hu
- 2. Jiongli Zhu
- 3. Boris Glavic
- 4. Babak Salimi
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,612 | Detecting Data Errors: Where are we and what needs to be done? | 2016 | VLDB | 0.00011142794 |
| 2,302 | Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions | 2021 | VLDB | 9.0668832e-05 |
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