Sparse linear algebra includes the fundamental and important operations in various large-
scale scientific computing and real-world applications. There exists performance bottleneck …

The low-rank canonical polyadic tensor decomposition is useful in data analysis and can be
computed by solving a sequence of overdetermined least squares subproblems. Motivated …

We propose a sampling-based method for computing the tensor ring (TR) decomposition of
a data tensor. The method uses leverage score sampled alternating least squares to fit the …

Recent papers have developed alternating least squares (ALS) methods for CP and tensor
ring decomposition with a per-iteration cost which is sublinear in the number of input tensor …

Multi-dimensional, large-scale, and sparse data, which can be neatly represented by sparse
tensors, are increasingly used in various applications such as data analysis and machine …

Many fields of scientific simulation, such as chemistry and condensed matter physics, are
increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this …

We propose a new fast randomized algorithm for interpolative decomposition of matrices
which utilizes CountSketch. We then extend this approach to the tensor interpolative …

This work considers the canonical polyadic decomposition (CPD) of tensors using
proximally regularized sketched alternating least squares algorithms. First, it establishes a …

Given taxi-ride counts information between departure and destination locations, how can we
forecast their future demands? In general, given a data stream of events with seasonal …

Sketching is a randomized dimensionality-reduction method that aims to preserve relevant
information in large-scale datasets. Count sketch is a simple popular sketch which uses a …