Although deep networks are typically used to approximate functions over high dimensional
inputs, recent work has increased interest in neural networks as function approximators for …

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 …

Given a separation oracle for a convex set K⊂ ℝ n that is contained in a box of radius R, the
goal is to either compute a point in K or prove that K does not contain a ball of radius є. We …

This article characterizes the exact asymptotics of random Fourier feature (RFF) regression,
in the realistic setting where the number of data samples $ n $, their dimension $ p $, and …

Random Fourier features is a widely used, simple, and effective technique for scaling up
kernel methods. The existing theoretical analysis of the approach, however, remains …

Kernel methods are fundamental tools in machine learning that allow detection of non-linear
dependencies between data without explicitly constructing feature vectors in high …

Kernel methods are fundamental in machine learning, and faster algorithms for kernel
approximation provide direct speedups for many core tasks in machine learning. The …

The slow convergence rate and pathological curvature issues of first-order gradient methods
for training deep neural networks, initiated an ongoing effort for developing faster $\mathit …

Abstract The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide
memorization, optimization and generalization guarantees in deep neural networks. A line of …

We present a novel mechanism to improve the accuracy of the recently-introduced class of
graph random features (GRFs). Our method induces negative correlations between the …