Recent and Upcoming Developments in Randomized Numerical Linear Algebra
for Machine Learning

Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning

17 June 2024

Michał Dereziński

Michael W. Mahoney

Papers citing "Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning"

9 / 9 papers shown

Title
Position: Curvature Matrices Should Be Democratized via Linear Operators Felix Dangel Runa Eschenhagen Weronika Ormaniec Andres Fernandez Lukas Tatzel Agustinus Kristiadi 58 3 0 31 Jan 2025
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning Michal Dereziñski Christopher Musco Jiaming Yang 42 2 0 09 May 2024
Distributed Least Squares in Small Space via Sketching and Bias Reduction Sachin Garg Kevin Tan Michal Dereziñski 28 1 0 08 May 2024
Stochastic Rounding Implicitly Regularizes Tall-and-Thin Matrices Gregory Dexter Christos Boutsikas Linkai Ma Ilse C. F. Ipsen P. Drineas 21 3 0 18 Mar 2024
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 52 5 0 06 Jun 2022
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update Michal Derezinski Jonathan Lacotte Mert Pilanci Michael W. Mahoney 40 26 0 15 Jul 2021
Reverse iterative volume sampling for linear regression Michal Derezinski Manfred K. Warmuth 49 43 0 06 Jun 2018
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 86 277 0 09 Aug 2012
Determinantal point processes for machine learning Alex Kulesza B. Taskar 162 1,123 0 25 Jul 2012