On the Benefits of Large Learning Rates for Kernel Methods
This work addresses the problem of optimizing learning rates for improved generalization in kernel methods, providing theoretical insights that are incremental but clarify phenomena observed in deep learning.
The paper investigates the generalization benefits of using large learning rates in gradient descent for kernel methods, showing that early stopping with such rates influences the solution's spectral decomposition on Hessian eigenvectors, extending insights from a toy problem to realistic scenarios like kernel ridge regression.
This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms. First observed in the deep learning literature, we show that a phenomenon can be precisely characterized in the context of kernel methods, even though the resulting optimization problem is convex. Specifically, we consider the minimization of a quadratic objective in a separable Hilbert space, and show that with early stopping, the choice of learning rate influences the spectral decomposition of the obtained solution on the Hessian's eigenvectors. This extends an intuition described by Nakkiran (2020) on a two-dimensional toy problem to realistic learning scenarios such as kernel ridge regression. While large learning rates may be proven beneficial as soon as there is a mismatch between the train and test objectives, we further explain why it already occurs in classification tasks without assuming any particular mismatch between train and test data distributions.