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

Recent studies indicate that kernel machines can often perform similarly or better than deep
neural networks (DNNs) on small datasets. The interest in kernel machines has been …

In recent years, there has been widespread adoption of machine learning-based
approaches to automate the solving of partial differential equations (PDEs). Among these …

In the study of stochastic systems, the committor function describes the probability that a
system starting from an initial configuration x will reach a set B before a set A. This paper …

This survey explores modern approaches for computing low-rank approximations of high-
dimensional matrices by means of the randomized SVD, randomized subspace iteration …

Proteins and the complexes they form are central to nearly all cellular processes. Their
flexibility, expressed through a continuum of states, provides a window into their biological …

This paper introduces two randomized preconditioning techniques for robustly solving
kernel ridge regression (KRR) problems with a medium to large number of data points ($10 …

We revisit data selection in a modern context of finetuning from a fundamental perspective.
Extending the classical wisdom of variance minimization in low dimensions to high …

Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems
that range from computational chemistry to health analytics, with a particular interest due to …

Low-rank approximation of a matrix function,, is an important task in computational
mathematics. Most methods require direct access to, which is often considerably more …