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

This paper investigates two randomized preconditioning techniques for solving kernel ridge
regression (KRR) problems with a medium to large number of data points ($10^ 4\leq N\leq …

We propose a new randomized algorithm for solving L2-regularized least-squares problems
based on sketching. We consider two of the most popular random embeddings, namely …

We provide an exact analysis of a class of randomized algorithms for solving
overdetermined least-squares problems. We consider first-order methods, where the …

Neural networks (NNs) have been extremely successful across many tasks in machine
learning. Quantization of NN weights has become an important topic due to its impact on …

Iterative sketching and sketch-and-precondition are randomized algorithms used for solving
overdetermined linear least-squares problems. When implemented in exact arithmetic, these …

We investigate iterative methods with randomized preconditioners for solving
overdetermined least-squares problems, where the preconditioners are based on a random …

In this work, we address the problem of Hessian inversion bias in distributed second-order
optimization algorithms. We introduce a novel shrinkage-based estimator for the resolvent of …

One of the greatest success stories of randomized algorithms for linear algebra has been the
development of fast, randomized algorithms for highly overdetermined linear least-squares …

Algorithms from Randomized Numerical Linear Algebra (RandNLA) are known to be
effective in handling high-dimensional computational problems, providing high-quality …