The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …

We study the generalization properties of ridge regression with random features in the
statistical learning framework. We show for the first time that $ O (1/\sqrt {n}) $ learning …

We study a decomposition-based scalable approach to kernel ridge regression, and show
that it achieves minimax optimal convergence rates under relatively mild conditions. The …

Early stopping is a form of regularization based on choosing when to stop running an
iterative algorithm. Focusing on non-parametric regression in a reproducing kernel Hilbert …

We study distributed learning with the least squares regularization scheme in a reproducing
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …

We study a decomposition-based scalable approach to performing kernel ridge regression.
The method is simply described: it randomly partitions a dataset of size N into m subsets of …

Gaussian processes are a powerful framework for quantifying uncertainty and for sequential
decision-making but are limited by the requirement of solving linear systems. In general, this …

In order to minimize the generalization error in neural networks, a novel technique to identify
overfitting phenomena when training the learner is formally introduced. This enables support …

We consider the random-design least-squares regression problem within the reproducing
kernel Hilbert space (RKHS) framework. Given a stream of independent and identically …

Spectral algorithms have been widely used and studied in learning theory and inverse
problems. This paper is concerned with distributed spectral algorithms, for handling big data …