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

Large matrices arise in many machine learning and data analysis applications, including as
representations of datasets, graphs, model weights, and first and second-order derivatives …

We study the problem of communication-efficient distributed vector mean estimation, which
is a commonly used subroutine in distributed optimization and Federated Learning (FL) …

We generalize the leverage score sampling sketch for $\ell_2 $-subspace embeddings, to
accommodate sampling subsets of the transformed data, so that the sketching approach is …

Gradient coding is a method for mitigating straggling servers in a centralized computing
network that uses erasure-coding techniques to distributively carry out first-order …

Matrices and tensors are amongst the most common tools to represent and exploit
information. Some sources produce large quantities of data, and analyzing the information …

This paper introduces a randomized Householder QR factorization (RHQR). This
factorization can be used to obtain a well conditioned basis of a set of vectors and thus can …

With the advent of massive datasets, distributed techniques for processing information and
carrying out computations are expected to enable exceptional possibilities for engineering …