Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

This survey highlights the recent advances in algorithms for numerical linear algebra that
have come from the technique of linear sketching, whereby given a matrix, one first …

In this paper we provide new randomized algorithms with improved runtimes for solving
linear programs with two-sided constraints. In the special case of the minimum cost flow …

Low-distortion embeddings are critical building blocks for develo** random sampling and
random projection algorithms for common linear algebra problems. We show that, given a …

We study the ℓ1-low rank approximation problem, where for a given nxd matrix A and
approximation factor α≤ 1, the goal is to output a rank-k matrix  for which‖ A-Â‖ 1≤ α …

Coresets are one of the central methods to facilitate the analysis of large data. We continue
a recent line of research applying the theory of coresets to logistic regression. First, we show …

We present a novel global compression framework for deep neural networks that
automatically analyzes each layer to identify the optimal per-layer compression ratio, while …

Subset selection for the rank k approximation of an n× d matrix A offers improvements in the
interpretability of matrices, as well as a variety of computational savings. This problem is well …

We give a simple algorithm to efficiently sample the rows of a matrix while preserving the p-
norms of its product with vectors. Given an n* d matrix A, we find with high probability and in …

We present a technical survey on the state of the art approaches in data reduction and the
coreset framework. These include geometric decompositions, gradient methods, random …