Research map / Learning theory and algorithms / Combinatorial and search optimization
Versatile Verification of Tree Ensembles
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase ensemble size | Veritas · Random verification tasks (Allstate, California housing, Covertype, Higgs) | relative gap | Hurt |
| Increase tree depth | Veritas · Random verification tasks (Allstate, California housing, Covertype, Higgs) | relative gap | Hurt |
| Vary model size | Veritas · Random verification tasks (Allstate, California housing, Covertype, Higgs) | runtime | No clear effect |
| Use Veritas on the reported Allstate stress-test cases | Veritas · Allstate stress test | finding a lower bound within the timeout | Hurt |
Closest papers by meaning
- Robustness Verification of Tree-based Models (NeurIPS 2019 · similarity 0.93)
- Sensitivity Verification for Additive Decision Tree Ensembles (ICLR 2025 · similarity 0.92)
- Robustness Verification of Multi-Class Tree Ensembles (AAAI 2024 · similarity 0.90)
- Data Aware and Scalable Sensitivity Analysis for Decision Tree Ensembles (ICLR 2026 · similarity 0.90)
- Minimax Rates and Spectral Distillation for Tree Ensembles (NeurIPS 2026 · similarity 0.86)
- Learning Decision Trees and Forests with Algorithmic Recourse (ICML 2024 · similarity 0.85)
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