Abstract: Kernel matrices appear in machine learning and non-parametric statistics. Given N points in d dimensions and a kernel function that requires O(d) work to evaluate, we present an O(dN log ...

Abstract: The spectral behavior of kernel matrices built from complex multi-variate data is established in the asymptotic regime where both the number of observations and their dimensionality increase ...

We place ourselves in the setting of high-dimensional statistical inference where the number of variables p in a dataset of interest is of the same order of magnitude as the number of observations n.

This repository contains Python implementations of kernel methods tailored for Symmetric Positive Definite (SPD) matrices. It includes tools for custom kernel functions and machine learning tasks ...

Simple model that performs a single matrix multiplication (C = A * B) with A and B being symmetric matrices. A (torch.Tensor): Input matrix A, shape (N, N), symmetric. B (torch.Tensor): Input matrix B ...

The performance of multivariate kernel density estimates depends crucially on the choice of bandwidth matrix, but progress towards developing good bandwidth matrix selectors has been relatively slow.