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Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with
  Minimax Optimal Rates
v1v2 (latest)

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

22 May 2013
Yuchen Zhang
John C. Duchi
Martin J. Wainwright
ArXiv (abs)PDFHTML

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
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
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
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
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
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
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
KTBoost: Combined Kernel and Tree Boosting
Fabio Sigrist
85
27
0
11 Feb 2019
Distributed sequential method for analyzing massive data
Distributed sequential method for analyzing massive data
Zhanfeng Wang
Y. Chang
8
0
0
22 Dec 2018
Distributed Inference for Linear Support Vector Machine
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
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
Quantile Regression Under Memory Constraint
Xi Chen
Weidong Liu
Yichen Zhang
99
119
0
18 Oct 2018
Distributed linear regression by averaging
Distributed linear regression by averaging
Yan Sun
Yueqi Sheng
FedML
94
66
0
30 Sep 2018
Towards A Unified Analysis of Random Fourier Features
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
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
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
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Dong Yin
Yudong Chen
Kannan Ramchandran
Peter L. Bartlett
OODFedML
160
1,529
0
05 Mar 2018
Optimal Convergence for Distributed Learning with Stochastic Gradient
  Methods and Spectral Algorithms
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
Randomized incomplete UUU-statistics in high dimensions
Xiaohui Chen
Kengo Kato
84
42
0
03 Dec 2017
Manifold regularization based on Nystr{ö}m type subsampling
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
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
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
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
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
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
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
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
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
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
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
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
Distributed learning with regularized least squares
Shaobo Lin
Xin Guo
Ding-Xuan Zhou
171
191
0
11 Aug 2016
Efficient Distributed Learning with Sparsity
Efficient Distributed Learning with Sparsity
Jialei Wang
Mladen Kolar
Nathan Srebro
Tong Zhang
FedML
90
152
0
25 May 2016
Constructive neural network learning
Constructive neural network learning
Shaobo Lin
Jinshan Zeng
Xiaoqin Zhang
72
31
0
30 Apr 2016
Greedy Criterion in Orthogonal Greedy Learning
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
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
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
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
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
Nonparametric Bayesian Aggregation for Massive Data
Zuofeng Shang
Botao Hao
Guang Cheng
24
8
0
17 Aug 2015
Optimal Learning Rates for Localized SVMs
Optimal Learning Rates for Localized SVMs
Mona Meister
Ingo Steinwart
64
55
0
23 Jul 2015
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
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
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
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
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
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
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
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
Randomized maximum-contrast selection: subagging for large-scale regression
Jelena Bradic
70
13
0
14 Jun 2013
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