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 / 54 papers shown

Title
Supervised Kernel Thinning Albert Gong Kyuseong Choi Raaz Dwivedi 31 0 0 17 Oct 2024
Optimal Kernel Quantile Learning with Random Features Caixing Wang Xingdong Feng 62 0 0 24 Aug 2024
Byzantine-tolerant distributed learning of finite mixture models Qiong Zhang Jiahua Chen Jiahua Chen FedML 53 0 0 19 Jul 2024
Nonlinear Meta-Learning Can Guarantee Faster Rates Dimitri Meunier Zhu Li Arthur Gretton Samory Kpotufe 35 6 0 20 Jul 2023
Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and Outliers M. Sajid A. K. Malik M. Tanveer 39 14 0 15 Jul 2023
Distributed Gradient Descent for Functional Learning Zhan Yu Jun Fan Zhongjie Shi Ding-Xuan Zhou 23 1 0 12 May 2023
A review of distributed statistical inference Yuan Gao Weidong Liu Hansheng Wang Xiaozhou Wang Yibo Yan Riquan Zhang 16 42 0 13 Apr 2023
On the Optimality of Misspecified Spectral Algorithms Hao Zhang Yicheng Li Qian Lin 28 15 0 27 Mar 2023
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes Di Wang Yao Wang Shaojie Tang OffRL 21 1 0 21 Feb 2023
An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models Yufeng Zhang Boyi Liu Qi Cai Lingxiao Wang Zhaoran Wang 53 11 0 30 Dec 2022
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 33 9 0 20 Nov 2022
Communication-efficient Distributed Newton-like Optimization with Gradients and M-estimators Ziyan Yin 32 0 0 13 Jul 2022
Federated Data Analytics: A Study on Linear Models Xubo Yue Raed Al Kontar Ana María Estrada Gómez FedML 39 12 0 15 Jun 2022
Markov subsampling based Huber Criterion Tieliang Gong Yuxin Dong Hong Chen B. Dong Chen Li 21 2 0 12 Dec 2021
Nyström Regularization for Time Series Forecasting Zirui Sun Mingwei Dai Yao Wang Shao-Bo Lin AI4TS 27 2 0 13 Nov 2021
Quantifying Epistemic Uncertainty in Deep Learning Ziyi Huang Henry Lam Haofeng Zhang UQCV BDL UD PER 24 12 0 23 Oct 2021
Fast Sketching of Polynomial Kernels of Polynomial Degree Zhao Song David P. Woodruff Zheng Yu Lichen Zhang 26 40 0 21 Aug 2021
Oversampling Divide-and-conquer for Response-skewed Kernel Ridge Regression Jingyi Zhang Xiaoxiao Sun 27 0 0 13 Jul 2021
From inexact optimization to learning via gradient concentration Bernhard Stankewitz Nicole Mücke Lorenzo Rosasco 31 5 0 09 Jun 2021
An Accurate and Efficient Large-scale Regression Method through Best Friend Clustering Kun Li Liang Yuan Yunquan Zhang Gongwei Chen 18 0 0 22 Apr 2021
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 39 18 0 09 Nov 2020
Distributed Learning of Finite Gaussian Mixtures Qiong Zhang Jiahua Chen 49 8 0 20 Oct 2020
Generalized Leverage Score Sampling for Neural Networks Jason D. Lee Ruoqi Shen Zhao Song Mengdi Wang Zheng Yu 26 42 0 21 Sep 2020
Distributed ARIMA Models for Ultra-long Time Series Xiaoqian Wang Yanfei Kang Rob J. Hyndman Feng Li AI4TS 14 50 0 19 Jul 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 33 114 0 18 Jun 2020
Kernel Alignment Risk Estimator: Risk Prediction from Training Data Arthur Jacot Berfin cSimcsek Francesco Spadaro Clément Hongler Franck Gabriel 22 67 0 17 Jun 2020
Federated Accelerated Stochastic Gradient Descent Honglin Yuan Tengyu Ma FedML 30 172 0 16 Jun 2020
Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis Xi Chen Jason D. Lee He Li Yun Yang 28 6 0 05 Apr 2020
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing Michael Kapralov Navid Nouri Ilya P. Razenshteyn A. Velingker A. Zandieh 24 13 0 21 Mar 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 98 152 0 02 Mar 2020
Generalisation error in learning with random features and the hidden manifold model Federica Gerace Bruno Loureiro Florent Krzakala M. Mézard Lenka Zdeborová 25 166 0 21 Feb 2020
Fast Polynomial Kernel Classification for Massive Data Jinshan Zeng Minrun Wu Shao-Bo Lin Ding-Xuan Zhou TPM 16 5 0 24 Nov 2019
Communication-Efficient Local Decentralized SGD Methods Xiang Li Wenhao Yang Shusen Wang Zhihua Zhang 30 53 0 21 Oct 2019
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping Shusen Wang 23 2 0 24 Sep 2019
On the Convergence of FedAvg on Non-IID Data Xiang Li Kaixuan Huang Wenhao Yang Shusen Wang Zhihua Zhang FedML 112 2,290 0 04 Jul 2019
Communication-Efficient Accurate Statistical Estimation Jianqing Fan Yongyi Guo Kaizheng Wang 19 110 0 12 Jun 2019
Efficient online learning with kernels for adversarial large scale problems Rémi Jézéquel Pierre Gaillard Alessandro Rudi 16 12 0 26 Feb 2019
Distributed Inference for Linear Support Vector Machine Xiaozhou Wang Zhuoyi Yang Xi Chen Weidong Liu 19 64 0 29 Nov 2018
First-order Newton-type Estimator for Distributed Estimation and Inference Xi Chen Weidong Liu Yichen Zhang 32 48 0 28 Nov 2018
Quantile Regression Under Memory Constraint Xi Chen Weidong Liu Yichen Zhang 11 116 0 18 Oct 2018
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees H. Avron Michael Kapralov Cameron Musco Christopher Musco A. Velingker A. Zandieh 17 156 0 26 Apr 2018
Parallel Streaming Wasserstein Barycenters Matthew Staib Sebastian Claici Justin Solomon Stefanie Jegelka 16 88 0 21 May 2017
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent Yudong Chen Lili Su Jiaming Xu FedML 19 241 0 16 May 2017
Preserving Differential Privacy Between Features in Distributed Estimation C. Heinze-Deml Brian McWilliams N. Meinshausen 20 7 0 01 Mar 2017
Distributed inference for quantile regression processes S. Volgushev Shih-Kang Chao Guang Cheng 18 128 0 21 Jan 2017
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 21 36 0 12 Oct 2016
Distributed learning with regularized least squares Shaobo Lin Xin Guo Ding-Xuan Zhou 38 190 0 11 Aug 2016
Efficient Distributed Learning with Sparsity Jialei Wang Mladen Kolar Nathan Srebro Tong Zhang FedML 32 151 0 25 May 2016
Constructive neural network learning Shaobo Lin Jinshan Zeng Xiaoqin Zhang 14 31 0 30 Apr 2016
Greedy Criterion in Orthogonal Greedy Learning Lin Xu Shaobo Lin Jinshan Zeng Xia Liu Zongben Xu 12 10 0 20 Apr 2016