Sparse sketches with small inversion bias

21 November 2020

Papers citing "Sparse sketches with small inversion bias"

23 / 23 papers shown

Title
Möbius inversion and the bootstrap Florian Schäfer 22 0 0 11 Aug 2024
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning Michał Dereziński Michael W. Mahoney 25 5 0 17 Jun 2024
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems Michal Dereziñski Daniel LeJeune Deanna Needell E. Rebrova 32 3 0 09 May 2024
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning Michal Dereziñski Christopher Musco Jiaming Yang 40 2 0 09 May 2024
Distributed Least Squares in Small Space via Sketching and Bias Reduction Sachin Garg Kevin Tan Michal Dereziñski 26 1 0 08 May 2024
Optimal Embedding Dimension for Sparse Subspace Embeddings Shabarish Chenakkod Michal Dereziñski Xiaoyu Dong M. Rudelson 40 12 0 17 Nov 2023
Asymptotically free sketched ridge ensembles: Risks, cross-validation, and tuning Filip Szatkowski Daniel LeJeune 27 9 0 06 Oct 2023
Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems Younghyun Cho James Demmel Michal Derezinski Haoyun Li Hengrui Luo Michael W. Mahoney Riley Murray 29 5 0 30 Aug 2023
Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints Halyun Jeong Deanna Needell 13 1 0 20 Apr 2023
Asymptotics of the Sketched Pseudoinverse Daniel LeJeune Pratik V. Patil Hamid Javadi Richard G. Baraniuk R. Tibshirani 19 10 0 07 Nov 2022
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson Christopher van der Heide Fred Roosta Michael W. Mahoney UQCV 18 5 0 14 Oct 2022
Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition Michal Derezinski E. Rebrova 27 16 0 20 Aug 2022
Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs Michal Derezinski 23 5 0 21 Jun 2022
Fast Kernel Methods for Generic Lipschitz Losses via $p$ -Sparsified Sketches T. Ahmad Pierre Laforgue Florence dÁlché-Buc 19 5 0 08 Jun 2022
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 49 5 0 06 Jun 2022
Hessian Averaging in Stochastic Newton Methods Achieves Superlinear Convergence Sen Na Michal Derezinski Michael W. Mahoney 27 16 0 20 Apr 2022
pylspack: Parallel algorithms and data structures for sketching, column subset selection, regression and leverage scores Aleksandros Sobczyk Efstratios Gallopoulos 9 5 0 05 Mar 2022
On randomized sketching algorithms and the Tracy-Widom law Daniel Ahfock W. Astle S. Richardson 21 1 0 03 Jan 2022
Learning Linear Models Using Distributed Iterative Hessian Sketching Han Wang James Anderson 18 2 0 08 Dec 2021
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update Michal Derezinski Jonathan Lacotte Mert Pilanci Michael W. Mahoney 34 26 0 15 Jul 2021
Reverse iterative volume sampling for linear regression Michal Derezinski Manfred K. Warmuth 46 43 0 06 Jun 2018
Cleaning large correlation matrices: tools from random matrix theory J. Bun J. Bouchaud M. Potters 32 262 0 25 Oct 2016
An algorithm for the principal component analysis of large data sets N. Halko P. Martinsson Y. Shkolnisky M. Tygert 65 277 0 30 Jul 2010