Sharp analysis of low-rank kernel matrix approximations

9 August 2012

Papers citing "Sharp analysis of low-rank kernel matrix approximations"

43 / 43 papers shown

Title
Learning with Exact Invariances in Polynomial Time Ashkan Soleymani B. Tahmasebi Stefanie Jegelka P. Jaillet 75 0 0 27 Feb 2025
Estimating the Spectral Moments of the Kernel Integral Operator from Finite Sample Matrices Chanwoo Chun SueYeon Chung Daniel D. Lee 26 1 0 23 Oct 2024
Target alignment in truncated kernel ridge regression Arash A. Amini R. Baumgartner Dai Feng 14 3 0 28 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
Generalized Reference Kernel for One-class Classification Jenni Raitoharju Alexandros Iosifidis 15 2 0 01 May 2022
The Spectral Bias of Polynomial Neural Networks Moulik Choraria L. Dadi Grigorios G. Chrysos Julien Mairal V. Cevher 24 18 0 27 Feb 2022
Fast Sketching of Polynomial Kernels of Polynomial Degree Zhao-quan Song David P. Woodruff Zheng Yu Lichen Zhang 13 40 0 21 Aug 2021
Neural Operator: Learning Maps Between Function Spaces Nikola B. Kovachki Zong-Yi Li Burigede Liu Kamyar Azizzadenesheli K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 32 440 0 19 Aug 2021
Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations S. Cipolla J. Gondzio 19 8 0 09 Aug 2021
Statistical Optimality and Computational Efficiency of Nyström Kernel PCA Nicholas Sterge Bharath K. Sriperumbudur 27 8 0 19 May 2021
Deep Equals Shallow for ReLU Networks in Kernel Regimes A. Bietti Francis R. Bach 28 86 0 30 Sep 2020
Generalized Leverage Score Sampling for Neural Networks J. Lee Ruoqi Shen Zhao-quan Song Mengdi Wang Zheng Yu 18 42 0 21 Sep 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 21 69 0 01 Aug 2020
Kernel Alignment Risk Estimator: Risk Prediction from Training Data Arthur Jacot Berfin cSimcsek Francesco Spadaro Clément Hongler Franck Gabriel 22 66 0 17 Jun 2020
Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization Jonathan Lacotte Mert Pilanci 27 23 0 10 Jun 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 36 172 0 23 Apr 2020
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing Michael Kapralov Navid Nouri Ilya P. Razenshteyn A. Velingker A. Zandieh 19 13 0 21 Mar 2020
Diversity sampling is an implicit regularization for kernel methods Michaël Fanuel J. Schreurs Johan A. K. Suykens 19 14 0 20 Feb 2020
On the Effectiveness of Richardson Extrapolation in Machine Learning Francis R. Bach 13 9 0 07 Feb 2020
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping Shusen Wang 23 2 0 24 Sep 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 47 626 0 14 Aug 2019
High-Dimensional Optimization in Adaptive Random Subspaces Jonathan Lacotte Mert Pilanci Marco Pavone 27 16 0 27 Jun 2019
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$ -means Clustering Manuel Fernández David P. Woodruff T. Yasuda 18 6 0 15 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 18 241 0 27 Apr 2019
Spatial Analysis Made Easy with Linear Regression and Kernels Philip Milton E. Giorgi Samir Bhatt 21 18 0 22 Feb 2019
Relating Leverage Scores and Density using Regularized Christoffel Functions Edouard Pauwels Francis R. Bach Jean-Philippe Vert 11 20 0 21 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 6 155 0 26 Apr 2018
Distributed Adaptive Sampling for Kernel Matrix Approximation Daniele Calandriello A. Lazaric Michal Valko 27 23 0 27 Mar 2018
Learning Relevant Features of Data with Multi-scale Tensor Networks Tayssir Doghri 25 137 0 31 Dec 2017
Subsampling for Ridge Regression via Regularized Volume Sampling Michal Derezinski Manfred K. Warmuth 26 20 0 14 Oct 2017
On the Sampling Problem for Kernel Quadrature François‐Xavier Briol Chris J. Oates Jon Cockayne W. Chen Mark Girolami 20 29 0 11 Jun 2017
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds Shusen Wang Alex Gittens Michael W. Mahoney 30 127 0 09 Jun 2017
Randomized Clustered Nystrom for Large-Scale Kernel Machines Farhad Pourkamali Anaraki Stephen Becker 26 33 0 20 Dec 2016
Faster Kernel Ridge Regression Using Sketching and Preconditioning H. Avron K. Clarkson David P. Woodruff 32 121 0 10 Nov 2016
Distributed learning with regularized least squares Shaobo Lin Xin Guo Ding-Xuan Zhou 35 190 0 11 Aug 2016
Constructive neural network learning Shaobo Lin Jinshan Zeng Xiaoqin Zhang 14 31 0 30 Apr 2016
Generalization Properties of Learning with Random Features Alessandro Rudi Lorenzo Rosasco MLT 29 325 0 14 Feb 2016
NYTRO: When Subsampling Meets Early Stopping Tomás Angles Raffaello Camoriano Alessandro Rudi Lorenzo Rosasco 24 32 0 19 Oct 2015
Efficient SDP Inference for Fully-connected CRFs Based on Low-rank Decomposition Peng Wang Chunhua Shen A. Hengel BDL 27 18 0 07 Apr 2015
On Data Preconditioning for Regularized Loss Minimization Tianbao Yang R. L. Jin Shenghuo Zhu Qihang Lin 40 9 0 13 Aug 2014
Matrix Coherence and the Nystrom Method Ameet Talwalkar Afshin Rostamizadeh 96 88 0 09 Aug 2014
LOCO: Distributing Ridge Regression with Random Projections C. Heinze Brian McWilliams N. Meinshausen Gabriel Krummenacher 47 34 0 13 Jun 2014
Randomized co-training: from cortical neurons to machine learning and back again David Balduzzi OOD 51 14 0 24 Oct 2013