SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient

1 March 2017

Papers citing "SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient"

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Title
DSAG: A mixed synchronous-asynchronous iterative method for straggler-resilient learning A. Severinson E. Rosnes S. E. Rouayheb Alexandre Graell i Amat 22 2 0 27 Nov 2021
Random-reshuffled SARAH does not need a full gradient computations Aleksandr Beznosikov Martin Takáč 26 7 0 26 Nov 2021
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning Xiaoxiao Zhao Jinlong Lei Li Li Jie-bin Chen OffRL 18 2 0 25 Nov 2021
Federated Expectation Maximization with heterogeneity mitigation and variance reduction Aymeric Dieuleveut G. Fort Eric Moulines Geneviève Robin FedML 31 5 0 03 Nov 2021
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima Zixiang Chen Dongruo Zhou Quanquan Gu 43 1 0 25 Oct 2021
Nys-Newton: Nyström-Approximated Curvature for Stochastic Optimization Dinesh Singh Hardik Tankaria M. Yamada ODL 44 2 0 16 Oct 2021
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning Gen Li Laixi Shi Yuxin Chen Yuejie Chi OffRL 45 51 0 09 Oct 2021
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 19 1 0 30 Sep 2021
ErrorCompensatedX: error compensation for variance reduced algorithms Hanlin Tang Yao Li Ji Liu Ming Yan 32 10 0 04 Aug 2021
Enhanced Bilevel Optimization via Bregman Distance Feihu Huang Junyi Li Shangqian Gao Heng-Chiao Huang 27 33 0 26 Jul 2021
A general sample complexity analysis of vanilla policy gradient Rui Yuan Robert Mansel Gower A. Lazaric 79 62 0 23 Jul 2021
Provably Faster Algorithms for Bilevel Optimization Junjie Yang Kaiyi Ji Yingbin Liang 51 132 0 08 Jun 2021
Randomized Stochastic Variance-Reduced Methods for Multi-Task Stochastic Bilevel Optimization Zhishuai Guo Quan Hu Lijun Zhang Tianbao Yang 61 30 0 05 May 2021
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning Xin Zhang Jia Liu Zhengyuan Zhu Elizabeth S. Bentley 49 14 0 04 May 2021
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity Shaocong Ma Ziyi Chen Yi Zhou Shaofeng Zou 17 11 0 30 Mar 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 43 14 0 21 Mar 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark W. Schmidt Simon Lacoste-Julien 18 18 0 18 Feb 2021
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization Ran Xin U. Khan S. Kar 20 39 0 12 Feb 2021
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction Haishan Ye Wei Xiong Tong Zhang 16 16 0 30 Dec 2020
Faster Non-Convex Federated Learning via Global and Local Momentum Rudrajit Das Anish Acharya Abolfazl Hashemi Sujay Sanghavi Inderjit S. Dhillon Ufuk Topcu FedML 40 82 0 07 Dec 2020
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm G. Fort Eric Moulines Hoi-To Wai TPM 21 7 0 30 Nov 2020
SMG: A Shuffling Gradient-Based Method with Momentum Trang H. Tran Lam M. Nguyen Quoc Tran-Dinh 23 21 0 24 Nov 2020
Local SGD: Unified Theory and New Efficient Methods Eduard A. Gorbunov Filip Hanzely Peter Richtárik FedML 37 109 0 03 Nov 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark W. Schmidt Francis R. Bach Peter Richtárik 19 111 0 02 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 37 0 0 26 Aug 2020
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization Zhize Li Hongyan Bao Xiangliang Zhang Peter Richtárik ODL 31 126 0 25 Aug 2020
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization Tianyi Chen Yuejiao Sun W. Yin 48 81 0 25 Aug 2020
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient Hao Jia Xiao Zhang Jun Xu Wei Zeng Hao Jiang Xiao Yan Ji-Rong Wen 17 3 0 25 Jul 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 39 50 0 08 Jul 2020
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations Yossi Arjevani Y. Carmon John C. Duchi Dylan J. Foster Ayush Sekhari Karthik Sridharan 90 53 0 24 Jun 2020
An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives Qi Qi Zhishuai Guo Yi Tian Xu Rong Jin Tianbao Yang 33 44 0 17 Jun 2020
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm Majid Jahani M. Nazari R. Tappenden A. Berahas Martin Takávc ODL 19 10 0 06 Jun 2020
Momentum-based variance-reduced proximal stochastic gradient method for composite nonconvex stochastic optimization Yangyang Xu Yibo Xu 28 23 0 31 May 2020
Stochastic Recursive Momentum for Policy Gradient Methods Huizhuo Yuan Xiangru Lian Ji Liu Yuren Zhou 21 31 0 09 Mar 2020
Flexible numerical optimization with ensmallen Ryan R. Curtin Marcus Edel Rahul Prabhu S. Basak Zhihao Lou Conrad Sanderson 18 1 0 09 Mar 2020
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning Yifan Hu Siqi Zhang Xin Chen Niao He ODL 41 55 0 25 Feb 2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods Lam M. Nguyen Quoc Tran-Dinh Dzung Phan Phuong Ha Nguyen Marten van Dijk 39 78 0 19 Feb 2020
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization Quoc Tran-Dinh Nhan H. Pham Lam M. Nguyen 27 22 0 17 Feb 2020
Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 27 4 0 13 Feb 2020
Gradient tracking and variance reduction for decentralized optimization and machine learning Ran Xin S. Kar U. Khan 19 10 0 13 Feb 2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization Samuel Horváth Lihua Lei Peter Richtárik Michael I. Jordan 57 30 0 13 Feb 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
Adaptive Stochastic Optimization Frank E. Curtis K. Scheinberg ODL 16 29 0 18 Jan 2020
Variance Reduced Local SGD with Lower Communication Complexity Xian-Feng Liang Shuheng Shen Jingchang Liu Zhen Pan Enhong Chen Yifei Cheng FedML 42 152 0 30 Dec 2019
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks Zhaoxian Wu Qing Ling Tianyi Chen G. Giannakis FedML AAML 32 181 0 29 Dec 2019
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling Mojmír Mutný Michal Derezinski Andreas Krause 38 21 0 25 Oct 2019
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms Kaiyi Ji Zhe Wang Bowen Weng Yi Zhou Wei Zhang Yingbin Liang ODL 18 5 0 21 Oct 2019
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction Pan Xu F. Gao Quanquan Gu 31 83 0 18 Sep 2019
Stochastic First-order Methods for Convex and Nonconvex Functional Constrained Optimization Digvijay Boob Qi Deng Guanghui Lan 52 92 0 07 Aug 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