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Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates

22 May 2013

Papers citing "Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates"

48 / 148 papers shown

Title
Two-stage Best-scored Random Forest for Large-scale Regression H. Hang Yingyi Chen Johan A. K. Suykens 9 0 0 09 May 2019
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up Dominic Richards Patrick Rebeschini 59 13 0 08 May 2019
Improved Classification Rates for Localized SVMs Ingrid Blaschzyk Ingo Steinwart 29 5 0 04 May 2019
A Global Bias-Correction DC Method for Biased Estimation under Memory Constraint Lu Lin Feng Li 40 2 0 16 Apr 2019
WONDER: Weighted one-shot distributed ridge regression in high dimensions Yan Sun Yueqi Sheng OffRL 92 51 0 22 Mar 2019
Efficient online learning with kernels for adversarial large scale problems Rémi Jézéquel Pierre Gaillard Alessandro Rudi 77 13 0 26 Feb 2019
KTBoost: Combined Kernel and Tree Boosting Fabio Sigrist 85 27 0 11 Feb 2019
Distributed sequential method for analyzing massive data Zhanfeng Wang Y. Chang 8 0 0 22 Dec 2018
Distributed Inference for Linear Support Vector Machine Xiaozhou Wang Zhuoyi Yang Xi Chen Weidong Liu 100 65 0 29 Nov 2018
First-order Newton-type Estimator for Distributed Estimation and Inference Xi Chen Weidong Liu Yichen Zhang 97 51 0 28 Nov 2018
Quantile Regression Under Memory Constraint Xi Chen Weidong Liu Yichen Zhang 99 119 0 18 Oct 2018
Distributed linear regression by averaging Yan Sun Yueqi Sheng FedML 94 66 0 30 Sep 2018
Towards A Unified Analysis of Random Fourier Features Zhu Li Jean-François Ton Dino Oglic Dino Sejdinovic 52 5 0 24 Jun 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 110 156 0 26 Apr 2018
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces Junhong Lin Volkan Cevher 30 9 0 12 Mar 2018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates Dong Yin Yudong Chen Kannan Ramchandran Peter L. Bartlett OOD FedML 160 1,529 0 05 Mar 2018
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms Junhong Lin Volkan Cevher 82 34 0 22 Jan 2018
Randomized incomplete $U$ -statistics in high dimensions Xiaohui Chen Kengo Kato 84 42 0 03 Dec 2017
Manifold regularization based on Nystr{ö}m type subsampling Abhishake Rastogi Sivananthan Sampath 37 4 0 13 Oct 2017
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network Zheng-Chu Guo Lei Shi Qiang Wu 39 43 0 07 Aug 2017
Parallel Streaming Wasserstein Barycenters Matthew Staib Sebastian Claici Justin Solomon Stefanie Jegelka 97 90 0 21 May 2017
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent Yudong Chen Lili Su Jiaming Xu FedML 90 246 0 16 May 2017
Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method Shunan Zhao Ruiqi Liu Zuofeng Shang 29 11 0 08 Mar 2017
Preserving Differential Privacy Between Features in Distributed Estimation C. Heinze-Deml Brian McWilliams N. Meinshausen 80 7 0 01 Mar 2017
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging Shusen Wang Alex Gittens Michael W. Mahoney 82 84 0 16 Feb 2017
Distributed inference for quantile regression processes S. Volgushev Shih-Kang Chao Guang Cheng 546 131 0 21 Jan 2017
Spatial Decompositions for Large Scale SVMs P. Thomann Ingrid Blaschzyk Mona Meister Ingo Steinwart 43 21 0 01 Dec 2016
Minimax Error of Interpolation and Optimal Design of Experiments for Variable Fidelity Data Alexey Zaytsev Evgeny Burnaev 67 2 0 21 Oct 2016
Communication-efficient Distributed Sparse Linear Discriminant Analysis Lu Tian Quanquan Gu 69 23 0 15 Oct 2016
Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification Prateek Jain Sham Kakade Rahul Kidambi Praneeth Netrapalli Aaron Sidford MoMe 101 36 0 12 Oct 2016
Distributed learning with regularized least squares Shaobo Lin Xin Guo Ding-Xuan Zhou 171 191 0 11 Aug 2016
Efficient Distributed Learning with Sparsity Jialei Wang Mladen Kolar Nathan Srebro Tong Zhang FedML 90 152 0 25 May 2016
Constructive neural network learning Shaobo Lin Jinshan Zeng Xiaoqin Zhang 72 31 0 30 Apr 2016
Greedy Criterion in Orthogonal Greedy Learning Lin Xu Shaobo Lin Jinshan Zeng Xia Liu Zongben Xu 25 10 0 20 Apr 2016
Divide and Conquer Local Average Regression Xiangyu Chang Shaobo Lin Yao Wang MoMe 57 37 0 23 Jan 2016
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server Leonard Hasenclever Stefan Webb Thibaut Lienart Sebastian J. Vollmer Balaji Lakshminarayanan Charles Blundell Yee Whye Teh BDL 183 70 0 31 Dec 2015
Computational Limits of A Distributed Algorithm For Smoothing Spline Zuofeng Shang Guang Cheng 264 56 0 31 Dec 2015
Distributed Estimation and Inference with Statistical Guarantees Heather Battey Jianqing Fan Han Liu Junwei Lu Ziwei Zhu 88 83 0 17 Sep 2015
Nonparametric Bayesian Aggregation for Massive Data Zuofeng Shang Botao Hao Guang Cheng 24 8 0 17 Aug 2015
Optimal Learning Rates for Localized SVMs Mona Meister Ingo Steinwart 64 55 0 23 Jul 2015
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections C. Heinze Brian McWilliams N. Meinshausen 134 37 0 08 Jun 2015
On the Feasibility of Distributed Kernel Regression for Big Data Chen Xu Yongquan Zhang Runze Li 41 30 0 05 May 2015
GP-select: Accelerating EM using adaptive subspace preselection Jacquelyn A. Shelton Jan Gasthaus Zhenwen Dai Jörg Lücke Arthur Gretton 67 18 0 10 Dec 2014
Greedy metrics in orthogonal greedy learning Lin Xu Shaobo Lin Jinshan Zeng Zongben Xu 46 3 0 13 Nov 2014
Learning Theory for Distribution Regression Z. Szabó Bharath K. Sriperumbudur Barnabás Póczós Arthur Gretton OOD 114 140 0 08 Nov 2014
LOCO: Distributing Ridge Regression with Random Projections C. Heinze Brian McWilliams N. Meinshausen Gabriel Krummenacher 132 34 0 13 Jun 2014
Loss minimization and parameter estimation with heavy tails Daniel J. Hsu Sivan Sabato 202 188 0 07 Jul 2013
Randomized maximum-contrast selection: subagging for large-scale regression Jelena Bradic 70 13 0 14 Jun 2013