The ability in experiments to control the relative twist angle between successive layers in two-
dimensional (2D) materials offers an approach to manipulating their electronic properties; …

Spectral clustering refers to a family of well-known unsupervised learning algorithms. Rather
than attempting to cluster points in their native domain, one constructs a (usually sparse) …

We study the problem of estimating the trace of a matrix A that can only be accessed through
matrix-vector multiplication. We introduce a new randomized algorithm, Hutch++, which …

The problem of estimating the trace of matrix functions appears in applications ranging from
machine learning and scientific computing, to computational biology. This paper presents an …

We study the problem of sampling k-bandlimited signals on graphs. We propose two
sampling strategies that consist in selecting a small subset of nodes at random. The first …

Spectral clustering has become a popular technique due to its high performance in many
contexts. It comprises three main steps: create a similarity graph between N objects to …

Logarithms of determinants of large positive definite matrices appear ubiquitously in
machine learning applications including Gaussian graphical and Gaussian process models …

The FEAST method for solving large sparse eigenproblems is equivalent to subspace
iteration with an approximate spectral projector and implicit orthogonalization. This relation …

This paper is concerned with the fictitious eigenfrequency problem of the boundary integral
equation methods when solving exterior acoustic problems. A contour integral method is …

Chains of superconducting circuit devices provide a natural platform for studies of synthetic
bosonic quantum matter. Motivated by the recent experimental progress in realizing …