A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates

28 June 2018

Papers citing "A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates"

35 / 35 papers shown

Title
Efficient Algorithms for Empirical Group Distributional Robust Optimization and Beyond Dingzhi Yu Yu-yan Cai Wei Jiang Lijun Zhang 49 6 0 06 Mar 2024
OptEx: Expediting First-Order Optimization with Approximately Parallelized Iterations Yao Shu Jiongfeng Fang Y. He Fei Richard Yu 35 0 0 18 Feb 2024
Composite federated learning with heterogeneous data Jiaojiao Zhang Jiang Hu Mikael Johansson FedML 32 4 0 04 Sep 2023
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization Yongqiang Chen Kaiwen Zhou Yatao Bian Binghui Xie Bing Wu ... Kaili Ma Han Yang P. Zhao Bo Han James Cheng OOD OODD 11 34 0 15 Jun 2022
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization Runxue Bao Xidong Wu Wenhan Xian Heng-Chiao Huang 31 1 0 23 Apr 2022
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 21 1 0 30 Sep 2021
Asynchronous Stochastic Optimization Robust to Arbitrary Delays Alon Cohen Amit Daniely Yoel Drori Tomer Koren Mariano Schain 24 32 0 22 Jun 2021
Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums Kaiwen Zhou Lai Tian Anthony Man-Cho So James Cheng 20 10 0 25 May 2021
Distributed Learning Systems with First-order Methods Ji Liu Ce Zhang 16 44 0 12 Apr 2021
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums Chaobing Song Stephen J. Wright Jelena Diakonikolas 75 16 0 26 Feb 2021
Regularization in network optimization via trimmed stochastic gradient descent with noisy label Kensuke Nakamura Bong-Soo Sohn Kyoung-Jae Won Byung-Woo Hong NoLa 13 0 0 21 Dec 2020
Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely Slavomír Hanzely Samuel Horváth Peter Richtárik FedML 50 187 0 05 Oct 2020
Asynchronous Distributed Optimization with Stochastic Delays Margalit Glasgow Mary Wootters 17 3 0 22 Sep 2020
Random extrapolation for primal-dual coordinate descent Ahmet Alacaoglu Olivier Fercoq V. Cevher 16 16 0 13 Jul 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi Ma 53 23 0 18 Jun 2020
Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates Kaiwen Zhou Anthony Man-Cho So James Cheng 14 5 0 25 May 2020
Stochastic batch size for adaptive regularization in deep network optimization Kensuke Nakamura Stefano Soatto Byung-Woo Hong ODL 27 6 0 14 Apr 2020
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems Filip Hanzely D. Kovalev Peter Richtárik 35 17 0 11 Feb 2020
Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization Fanhua Shang Bingkun Wei Hongying Liu Yuanyuan Liu Jiacheng Zhuo 11 1 0 02 Dec 2019
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 27 30 0 22 Oct 2019
Randomized Iterative Methods for Linear Systems: Momentum, Inexactness and Gossip Nicolas Loizou 27 5 0 26 Sep 2019
Adaptive Weight Decay for Deep Neural Networks Kensuke Nakamura Byung-Woo Hong 6 41 0 21 Jul 2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham T. Dzung Lam M. Nguyen 27 49 0 08 Jul 2019
A Generic Acceleration Framework for Stochastic Composite Optimization A. Kulunchakov Julien Mairal 18 43 0 03 Jun 2019
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data Shicong Cen Huishuai Zhang Yuejie Chi Wei-neng Chen Tie-Yan Liu FedML 14 27 0 29 May 2019
One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods Filip Hanzely Peter Richtárik 21 26 0 27 May 2019
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization A. Kulunchakov Julien Mairal 8 27 0 07 May 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 32 44 0 25 Jan 2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop D. Kovalev Samuel Horváth Peter Richtárik 36 155 0 24 Jan 2019
ASVRG: Accelerated Proximal SVRG Fanhua Shang L. Jiao Kaiwen Zhou James Cheng Yan Ren Yufei Jin ODL 29 30 0 07 Oct 2018
Direct Acceleration of SAGA using Sampled Negative Momentum Kaiwen Zhou 13 45 0 28 Jun 2018
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning Fanhua Shang Kaiwen Zhou Hongying Liu James Cheng Ivor W. Tsang Lijun Zhang Dacheng Tao L. Jiao 29 65 0 26 Feb 2018
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 19 200 0 27 Dec 2017
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/ε)$ Yi Tian Xu Yan Yan Qihang Lin Tianbao Yang 52 25 0 13 Jul 2016
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 93 737 0 19 Mar 2014