Shahin: Faster Algorithms for Generating Explanations for Multiple Predictions
Summary: Shahin batch-processes explanations for multiple predictions, identifying and reusing overlapping perturbation computations. Generalizable to LIME, Anchor, SHAP, it delivers large speedups with minimal overhead and few modifications. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Sona Hasani
- 2. Saravanan Thirumuruganathan
- 3. Nick Koudas
- 4. Gautam Das
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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 |
|---|---|---|---|---|
| 179 | Efficient and Extensible Algorithms for Multi Query Optimization | 2000 | SIGMOD | 0.00037637319 |
| 317 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.0002798145 |
| 2,871 | Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations | 2019 | SIGMOD | 7.9800983e-05 |
| 4,397 | Smurf: Self-Service String Matching Using Random Forests | 2019 | VLDB | 6.2142485e-05 |
| 6,329 | Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse | 2018 | VLDB | 5.1030159e-05 |
| 6,984 | A Cost-based Optimizer for Gradient Descent Optimization | 2017 | SIGMOD | 4.8680519e-05 |
| 8,924 | Leveraging Similarity Joins for Signal Reconstruction | 2018 | VLDB | 4.4229886e-05 |
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