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 …

Change-of-variables (CoV) formulas allow to reduce complicated probability densities to
simpler ones by a learned transformation with tractable Jacobian determinant. They are thus …

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 …

For applications as varied as Bayesian neural networks, determinantal point processes,
elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must …

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

We introduce an algorithm for estimating the trace of a matrix function using implicit products
with a symmetric matrix. Existing methods for implicit trace estimation of a matrix function …

Randomized trace estimation is a popular and well-studied technique that approximates the
trace of a large-scale matrix B by computing the average of x^ T Bx x TB x for many samples …

Computation of the trace of a matrix function plays an important role in many scientific
computing applications, including applications in machine learning, computational physics …

We present randomized algorithms for estimating the trace and determinant of Hermitian
positive semi-definite matrices. The algorithms are based on subspace iteration, and access …

We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential
family models, with a general parametrization of the energy function that includes neural …