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06/21/2023
Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications
This survey explores modern approaches for computing low-rank approximat...
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04/24/2023
Robust, randomized preconditioning for kernel ridge regression
This paper introduces two randomized preconditioning techniques for robu...
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01/19/2023
XTrace: Making the most of every sample in stochastic trace estimation
The implicit trace estimation problem asks for an approximation of the t...
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07/13/2022
Jackknife Variability Estimation For Randomized Matrix Computations
Randomized algorithms based on sketching have become a workhorse tool in...
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10/29/2021
Fast Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems
This paper develops a new class of algorithms for general linear systems...
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10/06/2021
Randomized Nyström Preconditioning
This paper introduces the Nyström PCG algorithm for solving a symmetric ...
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10/01/2021
Randomized block Krylov methods for approximating extreme eigenvalues
Randomized block Krylov subspace methods form a powerful class of algori...
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09/20/2021
Learning to Forecast Dynamical Systems from Streaming Data
Kernel analog forecasting (KAF) is a powerful methodology for data-drive...
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04/30/2021
Tensor Random Projection for Low Memory Dimension Reduction
Random projections reduce the dimension of a set of vectors while preser...
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07/31/2019
Binary Component Decomposition Part I: The Positive-Semidefinite Case
This paper studies the problem of decomposing a low-rank positive-semide...
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07/31/2019
Binary component decomposition Part II: The asymmetric case
This paper studies the problem of decomposing a low-rank matrix into a f...
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02/22/2019
Streaming Low-Rank Matrix Approximation with an Application to Scientific Simulation
This paper argues that randomized linear sketching is a natural tool for...
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02/09/2019
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity
This paper develops a new storage-optimal algorithm that provably solves...
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10/29/2018
Concentration of the Intrinsic Volumes of a Convex Body
The intrinsic volumes are measures of the content of a convex body. This...
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09/28/2018
Fast state tomography with optimal error bounds
Projected least squares (PLS) is an intuitive and numerically cheap tech...
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06/18/2017
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Several important applications, such as streaming PCA and semidefinite p...
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02/22/2017
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
This paper concerns a fundamental class of convex matrix optimization pr...
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08/31/2016
Randomized single-view algorithms for low-rank matrix approximation
This paper develops a suite of algorithms for constructing low-rank appr...
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11/30/2015
Universality laws for randomized dimension reduction, with applications
Dimension reduction is the process of embedding high-dimensional data in...
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01/07/2015
An Introduction to Matrix Concentration Inequalities
In recent years, random matrices have come to play a major role in compu...
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02/18/2012
