Recently, data-driven and learning-based algorithms for low rank matrix approximation were
shown to outperform classical data-oblivious algorithms by wide margins in terms of …

In this note we illustrate how common matrix approximation methods, such as random
projection and random sampling, yield projection-cost-preserving sketches, as introduced in …

In this paper we consider the problem of recovering a low-rank Tucker approximation to a
massive tensor based solely on structured random compressive measurements. Crucially …

Tensor representations of data have great promise, since as the size of data grows both in
terms of dimensionality and modes, it becomes increasingly advantageous to employ …

Matrices are ubiquitous mathematical structures that arise throughout computer science. We
study fast algorithms for several central problems involving matrices, including eigenvalue …

Recently, in statistics and machine learning, the notion of Randomization in Numerical
Linear Algebra (RandNLA) has not only evolved as a vital new tool to design fast and …

Let us restate the theorem. Recapping notation, let A∈ Rn× d be an input matrix. Let a1,...,
an denote its rows. Let k be the target low rank, and let ϵ> 0 be an error parameter. Let S0∈ …