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Decision trees are widely adopted machine learning models due to their simplicity and explainability.
Robust decision trees against adversarial examples
Hongge Chen, Huan Zhang, Duane Boning, and Cho-Jui Hsieh · 1902
Earlier work this paper cites.
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J. Ross Quinlan · 1986
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