Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

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 …

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 …

Randomized numerical linear algebra-RandNLA, for short-concerns the use of
randomization as a resource to develop improved algorithms for large-scale linear algebra …

Randomized Numerical Linear Algebra (RandNLA) is an area which uses randomness,
most notably random sampling and random projection methods, to develop improved …

The randomly pivoted Cholesky algorithm (RPCholesky) computes a factorized rank‐kk
approximation of an N× NN*N positive‐semidefinite (psd) matrix. RPCholesky requires only …

The Sinkhorn" distance," a variant of the Wasserstein distance with entropic regularization, is
an increasingly popular tool in machine learning and statistical inference. However, the time …

Sketching and stochastic gradient methods are arguably the most common techniques to
derive efficient large scale learning algorithms. In this paper, we investigate their application …

The theory of Koopman operators allows to deploy non-parametric machine learning
algorithms to predict and analyze complex dynamical systems. Estimators such as principal …