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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"

50 / 148 papers shown
Title
From inexact optimization to learning via gradient concentration
From inexact optimization to learning via gradient concentration
Bernhard Stankewitz
Nicole Mücke
Lorenzo Rosasco
88
5
0
09 Jun 2021
Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory
Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory
Ruiqi Liu
Ganggang Xu
Zuofeng Shang
42
0
0
20 May 2021
Achieving Fairness with a Simple Ridge Penalty
Achieving Fairness with a Simple Ridge Penalty
M. Scutari
F. Panero
M. Proissl
FaML
87
14
0
18 May 2021
An Accurate and Efficient Large-scale Regression Method through Best
  Friend Clustering
An Accurate and Efficient Large-scale Regression Method through Best Friend Clustering
Kun Li
Liang Yuan
Yunquan Zhang
Gongwei Chen
53
0
0
22 Apr 2021
Randomization-based Machine Learning in Renewable Energy Prediction
  Problems: Critical Literature Review, New Results and Perspectives
Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives
Javier Del Ser
D. Casillas-Pérez
L. Cornejo-Bueno
Luis Prieto-Godino
J. Sanz-Justo
C. Casanova-Mateo
S. Salcedo-Sanz
AI4CE
70
44
0
26 Mar 2021
Semiparametric Bayesian Inference for Local Extrema of Functions in the
  Presence of Noise
Semiparametric Bayesian Inference for Local Extrema of Functions in the Presence of Noise
Meng Li
Zejian Liu
Cheng-han Yu
M. Vannucci
56
3
0
19 Mar 2021
Variance Reduced Median-of-Means Estimator for Byzantine-Robust
  Distributed Inference
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Jiyuan Tu
Weidong Liu
Xiaojun Mao
Xi Chen
51
20
0
04 Mar 2021
Communication-efficient Byzantine-robust distributed learning with
  statistical guarantee
Communication-efficient Byzantine-robust distributed learning with statistical guarantee
Xingcai Zhou
Le Chang
Pengfei Xu
Shaogao Lv
FedML
33
0
0
28 Feb 2021
Divide-and-conquer methods for big data analysis
Divide-and-conquer methods for big data analysis
Xueying Chen
Jerry Q. Cheng
Min‐ge Xie
47
9
0
22 Feb 2021
Total Stability of SVMs and Localized SVMs
Total Stability of SVMs and Localized SVMs
H. Köhler
A. Christmann
36
4
0
29 Jan 2021
Equivalence of Convergence Rates of Posterior Distributions and Bayes
  Estimators for Functions and Nonparametric Functionals
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals
Zejian Liu
Meng Li
44
2
0
27 Nov 2020
On Function Approximation in Reinforcement Learning: Optimism in the
  Face of Large State Spaces
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
Zhuoran Yang
Chi Jin
Zhaoran Wang
Mengdi Wang
Michael I. Jordan
97
18
0
09 Nov 2020
A Computationally Efficient Classification Algorithm in Posterior Drift
  Model: Phase Transition and Minimax Adaptivity
A Computationally Efficient Classification Algorithm in Posterior Drift Model: Phase Transition and Minimax Adaptivity
Ruiqi Liu
Kexuan Li
Zuofeng Shang
33
4
0
09 Nov 2020
Distributed Learning of Finite Gaussian Mixtures
Distributed Learning of Finite Gaussian Mixtures
Qiong Zhang
Jiahua Chen
104
8
0
20 Oct 2020
Generalized Leverage Score Sampling for Neural Networks
Generalized Leverage Score Sampling for Neural Networks
Jason D. Lee
Ruoqi Shen
Zhao Song
Mengdi Wang
Zheng Yu
71
43
0
21 Sep 2020
Kernel-based L_2-Boosting with Structure Constraints
Kernel-based L_2-Boosting with Structure Constraints
Yao Wang
Xin Guo
Shao-Bo Lin
8
0
0
16 Sep 2020
Distributed ARIMA Models for Ultra-long Time Series
Distributed ARIMA Models for Ultra-long Time Series
Xiaoqian Wang
Yanfei Kang
Rob J. Hyndman
Feng Li
AI4TS
118
53
0
19 Jul 2020
Doubly Distributed Supervised Learning and Inference with
  High-Dimensional Correlated Outcomes
Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes
Emily C. Hector
P. Song
FedML
98
15
0
16 Jul 2020
Decentralised Learning with Random Features and Distributed Gradient
  Descent
Decentralised Learning with Random Features and Distributed Gradient Descent
Dominic Richards
Patrick Rebeschini
Lorenzo Rosasco
63
18
0
01 Jul 2020
Optimal Rates of Distributed Regression with Imperfect Kernels
Optimal Rates of Distributed Regression with Imperfect Kernels
Hongwei Sun
Qiang Wu
25
15
0
30 Jun 2020
Kernel methods through the roof: handling billions of points efficiently
Kernel methods through the roof: handling billions of points efficiently
Giacomo Meanti
Luigi Carratino
Lorenzo Rosasco
Alessandro Rudi
96
116
0
18 Jun 2020
Revisiting minimum description length complexity in overparameterized
  models
Revisiting minimum description length complexity in overparameterized models
Raaz Dwivedi
Chandan Singh
Bin Yu
Martin J. Wainwright
57
5
0
17 Jun 2020
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Arthur Jacot
Berfin cSimcsek
Francesco Spadaro
Clément Hongler
Franck Gabriel
80
68
0
17 Jun 2020
Federated Accelerated Stochastic Gradient Descent
Federated Accelerated Stochastic Gradient Descent
Honglin Yuan
Tengyu Ma
FedML
102
180
0
16 Jun 2020
Sample complexity and effective dimension for regression on manifolds
Sample complexity and effective dimension for regression on manifolds
Andrew D. McRae
Justin Romberg
Mark A. Davenport
106
8
0
13 Jun 2020
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression
  Estimators
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators
Zejian Liu
Meng Li
61
8
0
02 Jun 2020
Distributed Bayesian Varying Coefficient Modeling Using a Gaussian
  Process Prior
Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior
Rajarshi Guhaniyogi
Cheng Li
T. Savitsky
Sanvesh Srivastava
47
21
0
01 Jun 2020
Meta Clustering for Collaborative Learning
Meta Clustering for Collaborative Learning
Chenglong Ye
R. Ghanadan
Jie Ding
111
4
0
29 May 2020
Distributed Estimation for Principal Component Analysis: an Enlarged
  Eigenspace Analysis
Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis
Xi Chen
Jason D. Lee
He Li
Yun Yang
79
6
0
05 Apr 2020
Distributed Kernel Ridge Regression with Communications
Distributed Kernel Ridge Regression with Communications
Shao-Bo Lin
Di Wang
Ding-Xuan Zhou
41
34
0
27 Mar 2020
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Michael Kapralov
Navid Nouri
Ilya P. Razenshteyn
A. Velingker
A. Zandieh
83
13
0
21 Mar 2020
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and
  RKHS
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS
Yong Liu
Lizhong Ding
Weiping Wang
27
0
0
09 Mar 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy
  Regime
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime
Stéphane dÁscoli
Maria Refinetti
Giulio Biroli
Florent Krzakala
186
153
0
02 Mar 2020
Federated Learning for Resource-Constrained IoT Devices: Panoramas and
  State-of-the-art
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art
Ahmed Imteaj
Urmish Thakker
Shiqiang Wang
Jian Li
M. Amini
80
62
0
25 Feb 2020
Generalisation error in learning with random features and the hidden
  manifold model
Generalisation error in learning with random features and the hidden manifold model
Federica Gerace
Bruno Loureiro
Florent Krzakala
M. Mézard
Lenka Zdeborová
87
172
0
21 Feb 2020
Distributed Learning with Dependent Samples
Distributed Learning with Dependent Samples
Zirui Sun
Shao-Bo Lin
53
7
0
10 Feb 2020
Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms
Xiangyu Chang
Shao-Bo Lin
46
0
0
09 Jan 2020
Histogram Transform Ensembles for Large-scale Regression
Histogram Transform Ensembles for Large-scale Regression
H. Hang
Zhouchen Lin
Xiaoyu Liu
Hongwei Wen
16
2
0
08 Dec 2019
Fast Polynomial Kernel Classification for Massive Data
Fast Polynomial Kernel Classification for Massive Data
Jinshan Zeng
Minrun Wu
Shao-Bo Lin
Ding-Xuan Zhou
TPM
67
5
0
24 Nov 2019
$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning
  with Application to Clustering
DC2DC^2DC2: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering
Ke Alexander Wang
Xinran Bian
Pan Liu
Donghui Yan
120
4
0
16 Nov 2019
Distributed Networked Learning with Correlated Data
Distributed Networked Learning with Correlated Data
Lingzhou Hong
Alfredo García
Ceyhun Eksin
FedML
39
1
0
28 Oct 2019
Communication-Efficient Local Decentralized SGD Methods
Communication-Efficient Local Decentralized SGD Methods
Xiang Li
Wenhao Yang
Shusen Wang
Zhihua Zhang
97
53
0
21 Oct 2019
Stacked Autoencoder Based Deep Random Vector Functional Link Neural
  Network for Classification
Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification
Rakesh Katuwal
Ponnuthurai Nagaratnam Suganthan
58
98
0
04 Oct 2019
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature
  Mapping
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping
Shusen Wang
77
2
0
24 Sep 2019
Federated Learning: Challenges, Methods, and Future Directions
Federated Learning: Challenges, Methods, and Future Directions
Tian Li
Anit Kumar Sahu
Ameet Talwalkar
Virginia Smith
FedML
170
4,570
0
21 Aug 2019
Learning over inherently distributed data
Learning over inherently distributed data
Donghui Yan
Ying Xu
FedML
125
2
0
30 Jul 2019
On the Convergence of FedAvg on Non-IID Data
On the Convergence of FedAvg on Non-IID Data
Xiang Li
Kaixuan Huang
Wenhao Yang
Shusen Wang
Zhihua Zhang
FedML
220
2,363
0
04 Jul 2019
Random Vector Functional Link Neural Network based Ensemble Deep
  Learning
Random Vector Functional Link Neural Network based Ensemble Deep Learning
Rakesh Katuwal
Ponnuthurai Nagaratnam Suganthan
M. Tanveer
51
160
0
30 Jun 2019
Communication-Efficient Accurate Statistical Estimation
Communication-Efficient Accurate Statistical Estimation
Jianqing Fan
Yongyi Guo
Kaizheng Wang
61
114
0
12 Jun 2019
Towards Sharp Analysis for Distributed Learning with Random Features
Towards Sharp Analysis for Distributed Learning with Random Features
Jian Li
Yong Liu
Weiping Wang
62
3
0
07 Jun 2019
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