Efficient Training of Robust Decision Trees Against Adversarial Examples
D.A. Vos (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S.E. Verwer (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Recently it has been shown that many machine learning models are vulnerable to adversarial examples: perturbed samples that trick the model into misclassifying them. Neural networks have received much attention but decision trees and their ensembles achieve state-of-the-art results on tabular data, motivating research on their robustness. Recently the first methods have been proposed to train decision trees and their ensembles robustly [4, 3, 2, 1] but the state-of-the-art methods are expensive to run.