Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test
Summary: Proposes most comprehensible counterfactual explanations for KS-test failures, encoding user domain knowledge to clarify why a test set fails. MOCHE (MOst CompreHensible Explanation) is an efficient algorithm that avoids exponential enumeration, guarantees optimal explanations, and scales to real datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Zicun Cong (Simon Fraser University)
- 2. Lingyang Chu (McMaster University)
- 3. Yu Yang (City University of Hong Kong)
- 4. Jian Pei (Simon Fraser University)
BibTeX Citation
@article{cong_vldb21,
title = {{Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test}},
author = {Cong, Zicun and Chu, Lingyang and Yang, Yu and Pei, Jian},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {9},
pages = {1583--1596},
doi = {10.14778/3461535.3461546},
url = {https://doi.org/10.14778/3461535.3461546},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,716 | Counterfactual Explanation of Shapley Value in Data Coalitions | 2024 | VLDB | 5.6985704e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 142 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.00029189529 |
| 311 | Order Preserving Encryption for Numeric Data | 2004 | SIGMOD | 0.00021317851 |
| 583 | Efficient Algorithms for Mining Outliers from Large Data Sets | 2000 | SIGMOD | 0.00015952617 |
| 1,367 | Resisting Structural Re-identification in Anonymized Social Networks | 2008 | VLDB | 0.00010905843 |
| 2,360 | Online Outlier Detection in Sensor Data Using Non-Parametric Models | 2006 | VLDB | 8.5774153e-05 |
| 2,376 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB | 8.5517908e-05 |
| 3,679 | Detecting Change in Data Streams | 2004 | VLDB | 7.1016619e-05 |
| 3,738 | Learning to Validate the Predictions of Black Box Classifiers on Unseen Data | 2020 | SIGMOD | 7.0609398e-05 |
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