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
Trial and Trust: Addressing Byzantine Attacks with Comprehensive Defense Strategy Gleb Molodtsov Daniil Medyakov Sergey Skorik Nikolas Khachaturov Shahane Tigranyan Vladimir Aletov A. Avetisyan Martin Takáč Aleksandr Beznosikov AAML 35 0 0 12 May 2025
Adaptive Extrapolated Proximal Gradient Methods with Variance Reduction for Composite Nonconvex Finite-Sum Minimization Ganzhao Yuan 43 0 0 28 Feb 2025
A single-loop SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization Wei Liu Yangyang Xu 74 3 0 31 Jan 2025
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition Zhong Zheng Haochen Zhang Lingzhou Xue OffRL 78 2 0 10 Oct 2024
Adaptive Batch Size for Privately Finding Second-Order Stationary Points Daogao Liu Kunal Talwar 186 0 0 10 Oct 2024
Extended convexity and smoothness and their applications in deep learning Binchuan Qi Wei Gong Li Li 63 0 0 08 Oct 2024
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation Marina Sheshukova Denis Belomestny Alain Durmus Eric Moulines Alexey Naumov S. Samsonov 41 1 0 07 Oct 2024
Gradient-Free Method for Heavily Constrained Nonconvex Optimization Wanli Shi Hongchang Gao Bin Gu 21 5 0 31 Aug 2024
SOREL: A Stochastic Algorithm for Spectral Risks Minimization Yuze Ge Rujun Jiang 40 0 0 19 Jul 2024
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient Hao Di Haishan Ye Yueling Zhang Xiangyu Chang Guang Dai Ivor W. Tsang 40 1 0 28 May 2024
Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems Yanjie Zhong Jiaqi Li Soumendra Lahiri 29 1 0 29 Jan 2024
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization Ling Liang Haizhao Yang 14 1 0 23 Jan 2024
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning Prashant Khanduri Chengyin Li Rafi Ibn Sultan Yao Qiang Joerg Kliewer Dongxiao Zhu 45 0 0 21 Nov 2023
Efficiently Escaping Saddle Points for Non-Convex Policy Optimization Sadegh Khorasani Saber Salehkaleybar Negar Kiyavash Niao He Matthias Grossglauser 29 1 0 15 Nov 2023
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 36 1 0 09 Nov 2023
Adaptive Mirror Descent Bilevel Optimization Feihu Huang 40 1 0 08 Nov 2023
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization Zhen Qin Zhishuai Liu Pan Xu 26 1 0 24 Oct 2023
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates Ahmad Rammal Kaja Gruntkowska Nikita Fedin Eduard A. Gorbunov Peter Richtárik 45 5 0 15 Oct 2023
Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems Gabriel Mancino-Ball Yangyang Xu 20 8 0 14 Jul 2023
Federated Multi-Sequence Stochastic Approximation with Local Hypergradient Estimation Davoud Ataee Tarzanagh Mingchen Li Pranay Sharma Samet Oymak 34 0 0 02 Jun 2023
Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time Xiang Ji Gen Li OffRL 32 7 0 24 May 2023
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features Aleksandr Beznosikov David Dobre Gauthier Gidel 25 5 0 23 Apr 2023
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level Feihu Huang 32 18 0 07 Mar 2023
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics Andreas Krämer Aleksander E. P. Durumeric N. Charron Yaoyi Chen C. Clementi Frank Noé AI4CE 35 20 0 14 Feb 2023
Stochastic Dimension-reduced Second-order Methods for Policy Optimization Jinsong Liu Chen Xie Qinwen Deng Dongdong Ge Yi-Li Ye 32 1 0 28 Jan 2023
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization Le‐Yu Chen Jing Xu Luo Luo 31 15 0 16 Jan 2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction Bowen Lei Dongkuan Xu Ruqi Zhang Shuren He Bani Mallick 37 6 0 09 Jan 2023
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization G. Fort Eric Moulines 46 6 0 02 Jan 2023
Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis Quoc Tran-Dinh Marten van Dijk 34 0 0 19 Dec 2022
Variance-Reduced Conservative Policy Iteration Naman Agarwal Brian Bullins Karan Singh 32 3 0 12 Dec 2022
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization Xu Cai Chaobing Song Stephen J. Wright Jelena Diakonikolas 36 14 0 09 Dec 2022
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization Le‐Yu Chen Haishan Ye Luo Luo 73 5 0 05 Dec 2022
Zeroth-Order Alternating Gradient Descent Ascent Algorithms for a Class of Nonconvex-Nonconcave Minimax Problems Zi Xu Ziqi Wang Junlin Wang Y. Dai 26 11 0 24 Nov 2022
Adaptive Stochastic Optimisation of Nonconvex Composite Objectives Weijia Shao F. Sivrikaya S. Albayrak 21 0 0 21 Nov 2022
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods Yanli Liu Kaipeng Zhang Tamer Basar W. Yin 48 102 0 15 Nov 2022
Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization Ali Kavis Stratis Skoulakis Kimon Antonakopoulos L. Dadi V. Cevher 24 15 0 03 Nov 2022
SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities Aleksandr Beznosikov Alexander Gasnikov 40 2 0 12 Oct 2022
SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters Zhuqing Liu Xin Zhang Jia-Wei Liu 38 1 0 17 Aug 2022
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization Wei Jiang Gang Li Yibo Wang Lijun Zhang Tianbao Yang 35 16 0 18 Jul 2022
SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization Pourya Behmandpoor P. Latafat Andreas Themelis Marc Moonen Panagiotis Patrinos 29 2 0 17 Jul 2022
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology Yan Huang Ying Sun Zehan Zhu Changzhi Yan Jinming Xu FedML 30 15 0 08 Jul 2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen Trang H. Tran 32 2 0 13 Jun 2022
Stochastic Gradient Methods with Preconditioned Updates Abdurakhmon Sadiev Aleksandr Beznosikov Abdulla Jasem Almansoori Dmitry Kamzolov R. Tappenden Martin Takáč ODL 36 9 0 01 Jun 2022
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms Binghui Xie Chen Jin Kaiwen Zhou James Cheng Wei Meng 40 1 0 28 Apr 2022
AUC Maximization in the Era of Big Data and AI: A Survey Tianbao Yang Yiming Ying 44 179 0 28 Mar 2022
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization Wei Jiang Bokun Wang Yibo Wang Lijun Zhang Tianbao Yang 81 17 0 15 Feb 2022
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization Trang H. Tran K. Scheinberg Lam M. Nguyen 40 11 0 07 Feb 2022
Distributed Learning With Sparsified Gradient Differences Yicheng Chen Rick S. Blum Martin Takáč Brian M. Sadler 31 15 0 05 Feb 2022
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation Matilde Gargiani Andrea Zanelli Andrea Martinelli Tyler H. Summers John Lygeros 33 14 0 01 Feb 2022
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