This two-part comprehensive survey is devoted to a computing framework most commonly
known under the names Hyperdimensional Computing and Vector Symbolic Architectures …

Efficient learning of a data analysis task strongly depends on the data representation. Most
methods rely on (symmetric) similarity or dissimilarity representations by means of metric …

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 …

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 …

We consider the problem of training a $ d $ dimensional model with distributed differential
privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees …

We design a new distribution over m× n matrices S so that, for any fixed n× d matrix A of rank
r, with probability at least 9/10,∥ SAx∥ 2=(1±ε)∥ Ax∥ 2 simultaneously for all x∈ R d …

Similarity search—for example, identifying similar images in a database or similar
documents on the web—is a fundamental computing problem faced by large-scale …

Randomized algorithms for very large matrix problems have received a great deal of
attention in recent years. Much of this work was motivated by problems in large-scale data …

We show how to approximate a data matrix A with a much smaller sketch~ A that can be
used to solve a general class of constrained k-rank approximation problems to within (1+ ε) …

We propose a randomized second-order method for optimization known as the Newton
sketch: it is based on performing an approximate Newton step using a randomly projected …