Linganisha mbinu

	Mafunzo Amilifu Imara	Msitu Imara wa Misitu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2006	2000s–2010s
Mwanzilishi	Balcan, M.-F.; Beygelzimer, A.; Langford, J.	Various (extensions of Breiman 2001 Random Forest)
Aina	Active learning with robustness guarantees	Robust Ensemble (noise-tolerant bagging of decision trees)
Chanzo asilia	Balcan, M.-F., Beygelzimer, A., & Langford, J. (2006). Agnostic active learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), pp. 65–72. ACM.	Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18.
Majina mbadala	RAL, noise-tolerant active learning, robust query learning, adversarially robust active learning	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest
Zinazohusiana	6	6
Muhtasari	Robust Active Learning extends the standard active learning framework to handle noisy labels, adversarial perturbations, and unreliable oracles. Rather than assuming perfect labeling, it incorporates statistical or adversarial robustness guarantees into the query selection process, maintaining sample efficiency while tolerating corruption in the annotation process.	Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect.