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 …

Semidefinite programming (SDP) is a powerful framework from convex optimization that has
striking potential for data science applications. This paper develops a provably correct …

Cross Tensor Approximation (CTA) is a generalization of Cross/skeleton matrix and CUR
Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation. It …

Randomized numerical linear algebra-RandNLA, for short-concerns the use of
randomization as a resource to develop improved algorithms for large-scale linear algebra …

Matrices are exceptionally useful in various fields of study as they provide a convenient
framework to organize and manipulate data in a structured manner. However, modern …

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 …

This paper describes a new algorithm for computing a low-Tucker-rank approximation of a
tensor. The method applies a randomized linear map to the tensor to obtain a sketch that …

In order to safely deploy Deep Neural Networks (DNNs) within the perception pipelines of
real-time decision making systems, there is a need for safeguards that can detect out-of …

As the size of turbulent flow simulations continues to grow, in situ data compression is
becoming increasingly important for visualization, analysis, and restart checkpointing. For …

Big data analysis has become a crucial part of new emerging technologies such as the
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …