Research map / Adversarial robustness and security / Certified and empirical robustness
Efficient Training of Robust Decision Trees Against Adversarial Examples
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GROOT instead of a natural decision tree | Decision trees · 14 datasets, L∞ distance 0.1 attacks | adversarial accuracy: approximately 21% | Helped |
| Use GROOT instead of TREANT | Robust decision trees · 14 datasets, L∞ distance 0.1 attacks | adversarial accuracy | No clear effect |
| Use GROOT instead of Chen et al.'s heuristic | Robust decision trees · 14 datasets, L∞ distance 0.1 attacks | adversarial accuracy: 0.8% | No clear effect |
| Replace TREANT with GROOT | Robust decision tree training · 14 datasets | runtime: two orders of magnitude faster | Helped |
Closest papers by meaning
- Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees (ICML 2024 · similarity 0.89)
- TTTS: Tree Test Time Simulation for Enhancing Decision Tree Robustness against Adversarial Examples (AAAI 2024 · similarity 0.87)
- Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret (AAAI 2024 · similarity 0.85)
- Robust Loss Functions for Training Decision Trees with Noisy Labels (AAAI 2024 · similarity 0.85)
- Learning Decision Trees and Forests with Algorithmic Recourse (ICML 2024 · similarity 0.85)
- Faster Repeated Evasion Attacks in Tree Ensembles (NeurIPS 2024 · similarity 0.84)
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