Input Sparsity Time Low-Rank Approximation via Ridge Leverage Score Sampling

23 November 2015

Papers citing "Input Sparsity Time Low-Rank Approximation via Ridge Leverage Score Sampling"

27 / 27 papers shown

Title
Importance Sampling for Nonlinear Models Prakash Palanivelu Rajmohan Fred Roosta 12 0 0 18 May 2025
Provably Accurate Shapley Value Estimation via Leverage Score Sampling Christopher Musco R. Teal Witter FAtt FedML TDI 55 2 0 02 Oct 2024
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning Michal Dereziñski Christopher Musco Jiaming Yang 48 2 0 09 May 2024
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks Yongyi Yang Jiaming Yang Wei Hu Michal Dereziñski 48 0 0 26 Mar 2024
Learning the Positions in CountSketch Yi Li Honghao Lin Simin Liu A. Vakilian David P. Woodruff 29 18 0 11 Jun 2023
Provable Data Subset Selection For Efficient Neural Network Training M. Tukan Samson Zhou Alaa Maalouf Daniela Rus Vladimir Braverman Dan Feldman MLT 31 9 0 09 Mar 2023
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$ -Means Clustering Lingxiao Huang Zhize Li Jialin Sun Haoyu Zhao FedML 44 9 0 26 Oct 2022
Active Learning for Single Neuron Models with Lipschitz Non-Linearities Aarshvi Gajjar C. Hegde Christopher Musco 22 12 0 24 Oct 2022
ALLWAS: Active Learning on Language models in WASserstein space Anson Bastos Manohar Kaul MedIm 26 1 0 03 Sep 2021
Adversarial Robustness of Streaming Algorithms through Importance Sampling Vladimir Braverman Avinatan Hassidim Yossi Matias Mariano Schain Sandeep Silwal Samson Zhou AAML OOD 24 38 0 28 Jun 2021
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra Nadiia Chepurko K. Clarkson L. Horesh Honghao Lin David P. Woodruff 24 24 0 09 Nov 2020
EigenGame: PCA as a Nash Equilibrium I. Gemp Brian McWilliams Claire Vernade T. Graepel 32 46 0 01 Oct 2020
Generalized Leverage Score Sampling for Neural Networks J. Lee Ruoqi Shen Zhao Song Mengdi Wang Zheng Yu 21 42 0 21 Sep 2020
Efficient computation and analysis of distributional Shapley values Yongchan Kwon Manuel A. Rivas James Zou FAtt TDI 24 61 0 02 Jul 2020
The Statistical Cost of Robust Kernel Hyperparameter Tuning R. A. Meyer Christopher Musco 16 2 0 14 Jun 2020
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization Jonathan Lacotte Mert Pilanci 35 23 0 10 Jun 2020
An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications Haotian Jiang Y. Lee Zhao Song Sam Chiu-wai Wong 14 106 0 08 Apr 2020
Importance Sampling via Local Sensitivity Anant Raj Cameron Musco Lester W. Mackey 15 6 0 04 Nov 2019
Tight Sensitivity Bounds For Smaller Coresets Alaa Maalouf Adiel Statman Dan Feldman 32 18 0 02 Jul 2019
Spatial Analysis Made Easy with Linear Regression and Kernels Philip Milton E. Giorgi Samir Bhatt 27 18 0 22 Feb 2019
Randomized Iterative Algorithms for Fisher Discriminant Analysis Agniva Chowdhury Jiasen Yang P. Drineas 22 8 0 09 Sep 2018
On Coresets for Logistic Regression Alexander Munteanu Chris Schwiegelshohn C. Sohler David P. Woodruff 30 108 0 22 May 2018
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees H. Avron Michael Kapralov Cameron Musco Christopher Musco A. Velingker A. Zandieh 17 156 0 26 Apr 2018
Distributed Adaptive Sampling for Kernel Matrix Approximation Daniele Calandriello A. Lazaric Michal Valko 30 24 0 27 Mar 2018
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation? Cameron Musco David P. Woodruff 31 13 0 05 Nov 2017
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds Shusen Wang Alex Gittens Michael W. Mahoney 30 128 0 09 Jun 2017
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 86 280 0 09 Aug 2012