GeCo: Quality Counterfactual Explanations in Real Time
Summary: GeCo is the first real-time system for plausible, feasible counterfactual explanations, optimizing for minimal feature changes. Delta representations and partial classifier evaluation accelerate search, outperforming five prior systems in quality and latency. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Maximilian Schleich (University of Washington)
- 2. Zixuan Geng (University of Washington)
- 3. Yihong Zhang (University of Washington)
- 4. Dan Suciu (University of Washington)
BibTeX Citation
@article{schleich_vldb21,
title = {{GeCo: Quality Counterfactual Explanations in Real Time}},
author = {Schleich, Maximilian and Geng, Zixuan and Zhang, Yihong and Suciu, Dan},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {9},
pages = {1681--1693},
doi = {10.14778/3461535.3461555},
url = {https://doi.org/10.14778/3461535.3461555},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,054 | Explainable AI: Foundations, Applications, Opportunities for Data Management Research | 2022 | SIGMOD | 6.3843089e-05 |
| 6,079 | FACET: Robust Counterfactual Explanation Analytics | 2023 | SIGMOD | 5.9850223e-05 |
| 9,313 | Computing Rule-Based Explanations by Leveraging Counterfactuals | 2023 | VLDB | 5.289545e-05 |
| 10,263 | Local Stability of Rankings | 2026 | SIGMOD | 5.093636e-05 |
| 11,544 | CFDB: Machine Learning Model Analysis via Databases of CounterFactuals | 2022 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 24 | Spark SQL: Relational Data Processing in Spark | 2015 | SIGMOD | 0.00054865648 |
| 715 | Learning Generalized Linear Models Over Normalized Data | 2015 | SIGMOD | 0.00014655327 |
| 2,370 | Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning | 2019 | VLDB | 8.6795925e-05 |
| 2,488 | How to Architect a Query Compiler, Revisited | 2018 | SIGMOD | 8.5091578e-05 |
| 2,769 | A Layered Aggregate Engine for Analytics Workloads | 2019 | SIGMOD | 8.1465406e-05 |
| 2,987 | Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations | 2019 | SIGMOD | 7.8907997e-05 |
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