This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

This paper describes a suite of algorithms for constructing low-rank approximations of an
input matrix from a random linear image, or sketch, of the matrix. These methods can …

We investigate the adversarial robustness of streaming algorithms. In this context, an
algorithm is considered robust if its performance guarantees hold even if the stream is …

Recent Advances in Multi-Pass Graph Streaming Lower Bounds Page 1 Recent Advances in
Multi-Pass Graph Streaming Lower Bounds Sepehr Assadi Cheriton School of Computer …

This paper argues that randomized linear sketching is a natural tool for on-the-fly
compression of data matrices that arise from large-scale scientific simulations and data …

We study the problem of finding an approximate maximum matching in two closely related
computational models, namely, the dynamic graph streaming model and the simultaneous …

We study the problem of estimating the maximum matching size in graphs whose edges are
revealed in a streaming manner. We consider both insertion-only streams, which only …

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 …

Several important applications, such as streaming PCA and semidefinite programming,
involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of …

What guarantees are possible for solving logistic regression in one pass over a data
stream? To answer this question, we present the first data oblivious sketch for logistic …