方法对比

	鲁棒决策树	鲁棒梯度提升
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2000s–2019	2001
提出者	Various (Chen & Nan 2019; robust statistics community)	Friedman, J. H. (with Huber loss from Huber, P. J.)
类型	Supervised classification / regression tree	Ensemble (boosted trees with robust loss)
开创性文献	Chen, H., & Nan, F. (2019). Robust Decision Trees Against Adversarial Examples. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1006–1015.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
别名	robust tree, noise-tolerant decision tree, outlier-resistant decision tree, robust CART	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
相关	6	6
摘要	A Robust Decision Tree is a decision tree variant trained with modified splitting criteria or training procedures designed to reduce sensitivity to outliers, label noise, and adversarial perturbations. Rather than minimizing standard impurity measures that are strongly affected by extreme values, robust variants use statistically robust analogues or regularization to produce splits that generalize under noisy or corrupted data conditions.	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.