Random Feature Regression

Random feature regression (RFR) aims to accelerate and improve the scalability of kernel methods, such as Gaussian process regression, by approximating kernel functions using randomly generated features. Current research focuses on optimizing hyperparameters, developing novel feature generation techniques like Stein random features, and analyzing the generalization performance of RFR under various conditions, including high dimensionality and overparameterization. These advancements offer improved efficiency and theoretical understanding for large-scale regression problems, impacting both machine learning algorithms and their application in diverse fields.