Random feature methods provide scalable approximation to kernel ridge regression with a neighboring early-stopping rule that can adaptively select regularization parameters without prior knowledge of smoothness and capacity parameters

Random feature methods provide a scalable approximation to kernel ridge regression (KRR), but the regularization parameter that yields the oracle learning rate depends on unknown smoothness and capacity parameters. In this work, we propose a neighboring early-stopping rule for adaptive regularization in KRR with random features (KRR-RF).

http://arxiv.org/abs/2608.25513v1