Linganisha mbinu

	Mchakato Imara wa Gaussian	Msitu Imara wa Misitu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2011 (formal treatment); GP foundations: Rasmussen & Williams 2006	2000s–2010s
Mwanzilishi	Jylanki, P.; Vanhatalo, J.; Vehtari, A.	Various (extensions of Breiman 2001 Random Forest)
Aina	Probabilistic non-parametric regression / classification	Robust Ensemble (noise-tolerant bagging of decision trees)
Chanzo asilia	Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257.	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	Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest
Zinazohusiana	5	6
Muhtasari	Robust Gaussian Process (Robust GP) extends the standard Gaussian Process framework by replacing the Gaussian noise likelihood with a heavy-tailed distribution — typically Student-t — so that outliers in the training data exert less influence on the learned function. It retains the full probabilistic, uncertainty-quantifying character of a standard GP while becoming far less sensitive to corrupted or anomalous observations.	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.