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Distributed learning with regularized least squares

11 August 2016

Papers citing "Distributed learning with regularized least squares"

31 / 81 papers shown

Title
Generalised Lipschitz Regularisation Equals Distributional Robustness Zac Cranko Zhan Shi Xinhua Zhang Richard Nock Simon Kornblith OOD 86 21 0 11 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 53 0 0 09 Jan 2020
On the Improved Rates of Convergence for Matérn-type Kernel Ridge Regression, with Application to Calibration of Computer Models Rui Tuo Yan Wang C. F. Jeff Wu 59 28 0 01 Jan 2020
Realization of spatial sparseness by deep ReLU nets with massive data C. Chui Shao-Bo Lin Bo Zhang Ding-Xuan Zhou 35 23 0 16 Dec 2019
Histogram Transform Ensembles for Large-scale Regression H. Hang Zhouchen Lin Xiaoyu Liu Hongwei Wen 21 2 0 08 Dec 2019
Fast Polynomial Kernel Classification for Massive Data Jinshan Zeng Minrun Wu Shao-Bo Lin Ding-Xuan Zhou TPM 69 5 0 24 Nov 2019
Communication-Efficient Local Decentralized SGD Methods Xiang Li Wenhao Yang Shusen Wang Zhihua Zhang 97 53 0 21 Oct 2019
Distributed filtered hyperinterpolation for noisy data on the sphere Shao-Bo Lin Yu Guang Wang Ding-Xuan Zhou 18 19 0 06 Oct 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
Towards Sharp Analysis for Distributed Learning with Random Features Jian Li Yong Liu Weiping Wang 64 3 0 07 Jun 2019
Two-stage Best-scored Random Forest for Large-scale Regression H. Hang Yingyi Chen Johan A. K. Suykens 14 0 0 09 May 2019
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up Dominic Richards Patrick Rebeschini 61 13 0 08 May 2019
Improved Classification Rates for Localized SVMs Ingrid Blaschzyk Ingo Steinwart 34 5 0 04 May 2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning C. Chui Shao-Bo Lin Ding-Xuan Zhou 164 34 0 03 Apr 2019
WONDER: Weighted one-shot distributed ridge regression in high dimensions Yan Sun Yueqi Sheng OffRL 92 51 0 22 Mar 2019
Max-Diversity Distributed Learning: Theory and Algorithms Yong Liu Jian Li Weiping Wang 13 0 0 19 Dec 2018
Effective Parallelisation for Machine Learning Michael Kamp Mario Boley Olana Missura Thomas Gärtner 52 12 0 08 Oct 2018
Distributed linear regression by averaging Yan Sun Yueqi Sheng FedML 94 66 0 30 Sep 2018
Generalization Properties of hyper-RKHS and its Applications Fanghui Liu Lei Shi Xiaolin Huang Jie Yang Johan A. K. Suykens 48 4 0 26 Sep 2018
Analysis of regularized Nyström subsampling for regression functions of low smoothness Shuai Lu Peter Mathé S. Pereverzyev 67 18 0 03 Jun 2018
Universality of Deep Convolutional Neural Networks Ding-Xuan Zhou HAI PINN 428 518 0 28 May 2018
Learning through deterministic assignment of hidden parameters Jian Fang Shaobo Lin Zongben Xu 37 7 0 22 Mar 2018
Convergence of Online Mirror Descent Yunwen Lei Ding-Xuan Zhou 60 21 0 18 Feb 2018
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms Junhong Lin Volkan Cevher 82 34 0 22 Jan 2018
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces Junhong Lin Alessandro Rudi Lorenzo Rosasco Volkan Cevher 164 99 0 20 Jan 2018
Fast and Strong Convergence of Online Learning Algorithms Zheng-Chu Guo Lei Shi 39 14 0 10 Oct 2017
Universal Consistency and Robustness of Localized Support Vector Machines Florian Dumpert 55 17 0 19 Mar 2017
Parallelizing Spectral Algorithms for Kernel Learning Gilles Blanchard Nicole Mücke 67 15 0 24 Oct 2016
Divide and Conquer Local Average Regression Xiangyu Chang Shaobo Lin Yao Wang MoMe 69 37 0 23 Jan 2016
Optimal Learning Rates for Localized SVMs Mona Meister Ingo Steinwart 72 55 0 23 Jul 2015