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Distributed learning with regularized least squares

11 August 2016

Papers citing "Distributed learning with regularized least squares"

50 / 81 papers shown

Title
Learning Theory of Decentralized Robust Kernel-Based Learning Algorithm Zhan Yu 149 0 0 05 Jun 2025
Distributed Learning with Discretely Observed Functional Data Jiading Liu Lei Shi 50 0 0 03 Oct 2024
Optimal Kernel Quantile Learning with Random Features Caixing Wang Xingdong Feng 148 1 0 24 Aug 2024
Multiparameter regularization and aggregation in the context of polynomial functional regression E. R. Gizewski Markus Holzleitner Lukas Mayer-Suess S. Pereverzyev S. Pereverzyev 103 0 0 07 May 2024
Asymptotic Theory for Linear Functionals of Kernel Ridge Regression Rui Tuo Lu Zou 55 0 0 07 Mar 2024
Efficient Computing Algorithm for High Dimensional Sparse Support Vector Machine Jiawei Wen 62 1 0 25 Dec 2023
Adaptive Parameter Selection for Kernel Ridge Regression Shao-Bo Lin 26 3 0 10 Dec 2023
On regularized polynomial functional regression Markus Holzleitner S. Pereverzyev 215 6 0 06 Nov 2023
Distributed Uncertainty Quantification of Kernel Interpolation on Spheres Shao-Bo Lin Xingping Sun Di Wang 23 3 0 25 Oct 2023
Stability and Generalization for Minibatch SGD and Local SGD Yunwen Lei Tao Sun Mingrui Liu 91 4 0 02 Oct 2023
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos Di Wang Xiaotong Liu Shao-Bo Lin Ding-Xuan Zhou 75 0 0 08 Sep 2023
Distributed Semi-Supervised Sparse Statistical Inference Jiyuan Tu Weidong Liu Xiaojun Mao Mingyue Xu 43 1 0 17 Jun 2023
Lp- and Risk Consistency of Localized SVMs Hannes Köhler 117 0 0 16 May 2023
Distributed Gradient Descent for Functional Learning Zhan Yu Jun Fan Zhongjie Shi Ding-Xuan Zhou 61 3 0 12 May 2023
Random Smoothing Regularization in Kernel Gradient Descent Learning Liang Ding Tianyang Hu Jiahan Jiang Donghao Li Wei Cao Yuan Yao 68 6 0 05 May 2023
A review of distributed statistical inference Yuan Gao Weidong Liu Hansheng Wang Xiaozhou Wang Yibo Yan Riquan Zhang 74 43 0 13 Apr 2023
On the Optimality of Misspecified Spectral Algorithms Hao Zhang Yicheng Li Qian Lin 79 18 0 27 Mar 2023
Error analysis of regularized trigonometric linear regression with unbounded sampling: a statistical learning viewpoint Anna Scampicchio Elena Arcari Melanie Zeilinger 54 1 0 16 Mar 2023
Sketching with Spherical Designs for Noisy Data Fitting on Spheres Shao-Bo Lin Di Wang Ding-Xuan Zhou 57 2 0 08 Mar 2023
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes Di Wang Yao Wang Shaojie Tang OffRL 143 1 0 21 Feb 2023
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 85 12 0 20 Nov 2022
Local SGD in Overparameterized Linear Regression Mike Nguyen Charly Kirst Nicole Mücke 58 0 0 20 Oct 2022
Capacity dependent analysis for functional online learning algorithms Xin Guo Zheng-Chu Guo Lei Shi 65 20 0 25 Sep 2022
Coefficient-based Regularized Distribution Regression Yuan Mao Lei Shi Zheng-Chu Guo 105 4 0 26 Aug 2022
Distribution Regression with Sliced Wasserstein Kernels Dimitri Meunier Massimiliano Pontil C. Ciliberto OOD 72 17 0 08 Feb 2022
Radial Basis Function Approximation with Distributively Stored Data on Spheres Han Feng Shao-Bo Lin Ding-Xuan Zhou 26 8 0 05 Dec 2021
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling Xubo Yue Raed Al Kontar FedML 130 9 0 28 Nov 2021
Nyström Regularization for Time Series Forecasting Zirui Sun Mingwei Dai Yao Wang Shao-Bo Lin AI4TS 73 2 0 13 Nov 2021
Data splitting improves statistical performance in overparametrized regimes Nicole Mücke Enrico Reiss Jonas Rungenhagen Markus Klein 55 8 0 21 Oct 2021
Oversampling Divide-and-conquer for Response-skewed Kernel Ridge Regression Jingyi Zhang Xiaoxiao Sun 48 0 0 13 Jul 2021
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions Luigi Carratino Stefano Vigogna Daniele Calandriello Lorenzo Rosasco 55 7 0 23 Jun 2021
Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks A. Rosato Massimo Panella Denis Kleyko 79 18 0 02 Jun 2021
Uncertainty quantification for distributed regression V. Avanesov UQCV 22 0 0 24 May 2021
Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression Wei Cao Bing-Yi Jing 55 6 0 20 Apr 2021
Total Stability of SVMs and Localized SVMs H. Köhler A. Christmann 48 4 0 29 Jan 2021
Sparse Representations of Positive Functions via First and Second-Order Pseudo-Mirror Descent A. Chakraborty K. Rajawat Alec Koppel 62 3 0 13 Nov 2020
Provable Fictitious Play for General Mean-Field Games Qiaomin Xie Zhuoran Yang Zhaoran Wang Andreea Minca 84 18 0 08 Oct 2020
Kernel regression in high dimensions: Refined analysis beyond double descent Fanghui Liu Zhenyu Liao Johan A. K. Suykens 82 50 0 06 Oct 2020
Kernel-based L_2-Boosting with Structure Constraints Yao Wang Xin Guo Shao-Bo Lin 13 0 0 16 Sep 2020
Kernel Interpolation of High Dimensional Scattered Data Shao-Bo Lin Xiangyu Chang Xingping Sun 11 6 0 03 Sep 2020
Distributed Learning via Filtered Hyperinterpolation on Manifolds Guido Montúfar Yu Guang Wang 20 7 0 18 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
Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes J. Schreurs Michaël Fanuel Johan A. K. Suykens 62 2 0 24 Jun 2020
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning Lingxiao Wang Zhuoran Yang Zhaoran Wang 72 27 0 21 Jun 2020
Estimates on Learning Rates for Multi-Penalty Distribution Regression Zhan Yu D. Ho 23 0 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
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 128 176 0 23 Apr 2020
Distributed Kernel Ridge Regression with Communications Shao-Bo Lin Di Wang Ding-Xuan Zhou 49 34 0 27 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