How Divergent Is Your Data?
Summary: DivExplorer identifies divergent subgroups in data where classifiers misbehave, isolating regions with elevated FP/FN. It supports debugging and bias analysis for ethically charged domains, enabling focused data curation and model evaluation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Eliana Pastor (Politecnico di Torino)
- 2. Andrew Gavgavian (University of California Santa Cruz)
- 3. Elena Baralis (Politecnico di Torino)
- 4. Luca de Alfaro (University of California Santa Cruz)
BibTeX Citation
@article{pastor_vldb21,
title = {{How Divergent Is Your Data?}},
author = {Pastor, Eliana and Gavgavian, Andrew and Baralis, Elena and de Alfaro, Luca},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {2835--2838},
doi = {10.14778/3476311.3476357},
url = {https://doi.org/10.14778/3476311.3476357},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 3,091 | MithraCoverage: A System for Investigating Population Bias for Intersectional Fairness | 2020 | SIGMOD | 7.7684166e-05 |
| 3,190 | Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence | 2021 | SIGMOD | 7.6532441e-05 |
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