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)
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Authors
- 1. Zicun Cong
- 2. Lingyang Chu
- 3. Yu Yang
- 4. Jian Pei
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,321 | Counterfactual Explanation of Shapley Value in Data Coalitions | 2024 | VLDB | 4.7629325e-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 |
|---|---|---|---|---|
| 161 | LOF: Identifying Density-Based Local Outliers | 2000 | SIGMOD | 0.00039846974 |
| 324 | Order Preserving Encryption for Numeric Data | 2004 | SIGMOD | 0.00027444645 |
| 701 | Efficient Algorithms for Mining Outliers from Large Data Sets | 2000 | SIGMOD | 0.00017938417 |
| 1,571 | Resisting Structural Re-identification in Anonymized Social Networks | 2008 | VLDB | 0.00011318916 |
| 2,629 | Online Outlier Detection in Sensor Data Using Non-Parametric Models | 2006 | VLDB | 8.4160309e-05 |
| 2,644 | Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series | 2020 | VLDB | 8.3832357e-05 |
| 3,685 | Detecting Change in Data Streams | 2004 | VLDB | 6.8448674e-05 |
| 4,110 | Learning to Validate the Predictions of Black Box Classifiers on Unseen Data | 2020 | SIGMOD | 6.4428544e-05 |
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