ExTuNe: Explaining Tuple Non-conformance
Summary: ExTuNe defines data invariants—implicit multi-attribute constraints that capture dataset conformance—and uses causal interventions to attribute non-conformance to specific attributes. It ranks test tuples by violation degree and visualizes attribute responsibility with heat maps to enable explanations and trusted ML. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anna Fariha
- 2. Ashish Tiwari
- 3. Arjun Radhakrishna
- 4. Sumit Gulwani
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,202 | Conformance Constraint Discovery: Measuring Trust in Data-Driven Systems | 2021 | SIGMOD | 4.8023314e-05 |
| 7,926 | CoCo: Interactive Exploration of Conformance Constraints for Data Understanding and Data Cleaning | 2021 | SIGMOD | 4.6144554e-05 |
| 8,388 | FEDEX: An Explainability Framework for Data Exploration Steps | 2022 | VLDB | 4.5297787e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 214 | Scorpion: Explaining Away Outliers in Aggregate Queries | 2013 | VLDB | 0.0003363692 |
| 5,128 | CAPE: Explaining Outliers by Counterbalancing | 2019 | VLDB | 5.6758584e-05 |
| 5,205 | ANMAT: Automatic Knowledge Discovery and Error Detection through Pattern Functional Dependencies | 2019 | SIGMOD | 5.630869e-05 |
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