Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations

13 July 2022

Papers citing "Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations"

30 / 30 papers shown

Title
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 146 0 0 02 Mar 2025
Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels Damir Filipović P. Schneider 45 0 0 29 Oct 2024
Supervised Kernel Thinning Albert Gong Kyuseong Choi Raaz Dwivedi 132 0 0 17 Oct 2024
Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky Ethan N. Epperly J. Tropp R. Webber 52 5 0 04 Oct 2024
The fast committor machine: Interpretable prediction with kernels D. Aristoff Mats S. Johnson Gideon Simpson Robert J. Webber 50 5 0 16 May 2024
Randomly Pivoted Partial Cholesky: Random How? Stefan Steinerberger 19 1 0 17 Apr 2024
Kernel Methods are Competitive for Operator Learning Pau Batlle Matthieu Darcy Bamdad Hosseini H. Owhadi 58 40 0 26 Apr 2023
Robust, randomized preconditioning for kernel ridge regression Mateo Díaz Ethan N. Epperly Zachary Frangella J. Tropp R. Webber 59 12 0 24 Apr 2023
Toward Large Kernel Models Amirhesam Abedsoltan M. Belkin Parthe Pandit 77 17 0 06 Feb 2023
Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features Adityanarayanan Radhakrishnan Daniel Beaglehole Parthe Pandit M. Belkin FAtt MLT 53 13 0 28 Dec 2022
A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller Viktor Zaverkin Johannes Kastner Ingo Steinwart UQCV BDL GP 77 36 0 17 Mar 2022
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks Adityanarayanan Radhakrishnan George Stefanakis M. Belkin Caroline Uhler 59 26 0 31 Jul 2021
Kernel approximation on algebraic varieties Jason M. Altschuler P. Parrilo 32 5 0 04 Jun 2021
Finite Versus Infinite Neural Networks: an Empirical Study Jaehoon Lee S. Schoenholz Jeffrey Pennington Ben Adlam Lechao Xiao Roman Novak Jascha Narain Sohl-Dickstein 69 214 0 31 Jul 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 68 116 0 18 Jun 2020
Determinantal Point Processes in Randomized Numerical Linear Algebra Michal Derezinski Michael W. Mahoney 54 80 0 07 May 2020
Diversity sampling is an implicit regularization for kernel methods Michaël Fanuel J. Schreurs Johan A. K. Suykens 53 14 0 20 Feb 2020
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks Sanjeev Arora S. Du Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang Dingli Yu AAML 58 162 0 03 Oct 2019
Exact sampling of determinantal point processes with sublinear time preprocessing Michal Derezinski Daniele Calandriello Michal Valko 51 55 0 31 May 2019
On Fast Leverage Score Sampling and Optimal Learning Alessandro Rudi Daniele Calandriello Luigi Carratino Lorenzo Rosasco 46 82 0 31 Oct 2018
Distributed Adaptive Sampling for Kernel Matrix Approximation Daniele Calandriello A. Lazaric Michal Valko 112 24 0 27 Mar 2018
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 148 360 0 30 Oct 2017
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data J. Tropp A. Yurtsever Madeleine Udell Volkan Cevher 48 81 0 18 Jun 2017
FALKON: An Optimal Large Scale Kernel Method Alessandro Rudi Luigi Carratino Lorenzo Rosasco 75 196 0 31 May 2017
Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices Cameron Musco David P. Woodruff 46 58 0 11 Apr 2017
Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes Nima Anari S. Gharan A. Rezaei 47 130 0 16 Feb 2016
An Introduction to Matrix Concentration Inequalities J. Tropp 153 1,149 0 07 Jan 2015
OpenML: networked science in machine learning Joaquin Vanschoren Jan N. van Rijn B. Bischl Luís Torgo FedML AI4CE 160 1,322 0 29 Jul 2014
Determinantal point processes for machine learning Alex Kulesza B. Taskar 239 1,138 0 25 Jul 2012
A Tutorial on Spectral Clustering U. V. Luxburg 277 10,532 0 01 Nov 2007