Research map / Learning theory and algorithms / Prediction and decision losses
Evasion and Hardening of Tree Ensemble Classifiers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use adversarial boosting to add budgeted evading instances during training | BDT · MNIST 2 vs 6 | L0 evasion robustness | Helped |
| Use adversarial boosting to add budgeted evading instances during training | BDT · MNIST 2 vs 6 | L1 evasion robustness | Hurt |
| Use adversarial boosting to add budgeted evading instances during training | BDT · MNIST 2 vs 6 | L2 evasion robustness | Hurt |
| Use adversarial boosting to add budgeted evading instances during training | BDT · MNIST 2 vs 6 | L∞ evasion robustness | Hurt |
| Use adversarial boosting to add budgeted evading instances during training | BDT · MNIST 2 vs 6 test set | test error: 0.25% -> 0.20% | Helped |
| Use a boosted tree ensemble instead of the benchmark alternatives | BDT and RF · MNIST 2 vs 6 | evasion robustness across L0, L1, L2, and L∞ | Hurt |
| Use L1 rather than L2 regularization | linear logistic regression · MNIST 2 vs 6 | evasion robustness | Hurt |
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