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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"

50 / 148 papers shown

Title
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 Ruiqi Liu Ganggang Xu Zuofeng Shang 42 0 0 20 May 2021
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 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 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 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 Jiyuan Tu Weidong Liu Xiaojun Mao Xi Chen 51 20 0 04 Mar 2021
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 Xueying Chen Jerry Q. Cheng Min‐ge Xie 47 9 0 22 Feb 2021
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 Zejian Liu Meng Li 44 2 0 27 Nov 2020
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 Ruiqi Liu Kexuan Li Zuofeng Shang 33 4 0 09 Nov 2020
Distributed Learning of Finite Gaussian Mixtures Qiong Zhang Jiahua Chen 104 8 0 20 Oct 2020
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 Yao Wang Xin Guo Shao-Bo Lin 8 0 0 16 Sep 2020
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 Emily C. Hector P. Song FedML 98 15 0 16 Jul 2020
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 Hongwei Sun Qiang Wu 25 15 0 30 Jun 2020
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 Raaz Dwivedi Chandan Singh Bin Yu Martin J. Wainwright 57 5 0 17 Jun 2020
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 Honglin Yuan Tengyu Ma FedML 102 180 0 16 Jun 2020
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 Zejian Liu Meng Li 61 8 0 02 Jun 2020
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 Chenglong Ye R. Ghanadan Jie Ding 111 4 0 29 May 2020
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 Shao-Bo Lin Di Wang Ding-Xuan Zhou 41 34 0 27 Mar 2020
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 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 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 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 Federica Gerace Bruno Loureiro Florent Krzakala M. Mézard Lenka Zdeborová 87 172 0 21 Feb 2020
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 H. Hang Zhouchen Lin Xiaoyu Liu Hongwei Wen 16 2 0 08 Dec 2019
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 Ke Alexander Wang Xinran Bian Pan Liu Donghui Yan 120 4 0 16 Nov 2019
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 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 Rakesh Katuwal Ponnuthurai Nagaratnam Suganthan 58 98 0 04 Oct 2019
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 Tian Li Anit Kumar Sahu Ameet Talwalkar Virginia Smith FedML 170 4,570 0 21 Aug 2019
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 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 Rakesh Katuwal Ponnuthurai Nagaratnam Suganthan M. Tanveer 51 160 0 30 Jun 2019
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 Jian Li Yong Liu Weiping Wang 62 3 0 07 Jun 2019