Stochastic Variance Reduction for Nonconvex Optimization

19 March 2016

Papers citing "Stochastic Variance Reduction for Nonconvex Optimization"

50 / 124 papers shown

Title
Learning to Reason under Off-Policy Guidance Jianhao Yan Yafu Li Zican Hu Zhi Wang Ganqu Cui Xiaoye Qu Yu Cheng Yue Zhang OffRL LRM 44 1 0 21 Apr 2025
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Jingruo Sun Zachary Frangella Madeleine Udell 41 0 0 28 Jan 2025
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 42 1 0 28 May 2024
Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks Arnulf Jentzen Adrian Riekert 43 4 0 07 Feb 2024
Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems Yanjie Zhong Jiaqi Li Soumendra Lahiri 34 1 0 29 Jan 2024
Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints Hari Dahal Wei Liu Yangyang Xu 49 5 0 15 Nov 2023
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 44 1 0 09 Nov 2023
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization Zhen Qin Zhishuai Liu Pan Xu 31 1 0 24 Oct 2023
Efficient preconditioned stochastic gradient descent for estimation in latent variable models C. Baey Maud Delattre E. Kuhn Jean-Benoist Léger Sarah Lemler 23 4 0 22 Jun 2023
Efficient Stochastic Approximation of Minimax Excess Risk Optimization Lijun Zhang Haomin Bai W. Tu Ping Yang Yao Hu 16 4 0 31 May 2023
How to escape sharp minima with random perturbations Kwangjun Ahn Ali Jadbabaie S. Sra ODL 36 6 0 25 May 2023
An Adaptive Policy to Employ Sharpness-Aware Minimization Weisen Jiang Hansi Yang Yu Zhang James T. Kwok AAML 83 32 0 28 Apr 2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction Bowen Lei Dongkuan Xu Ruqi Zhang Shuren He Bani Mallick 44 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
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 38 14 0 09 Dec 2022
Generalized Gradient Flows with Provable Fixed-Time Convergence and Fast Evasion of Non-Degenerate Saddle Points Mayank Baranwal Param Budhraja V. Raj A. Hota 33 2 0 07 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 28 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 29 15 0 03 Nov 2022
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients Hualin Zhang Huan Xiong Bin Gu 35 7 0 04 Oct 2022
SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization Pourya Behmandpoor P. Latafat Andreas Themelis Marc Moonen Panagiotis Patrinos 34 2 0 17 Jul 2022
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation Pengfei Guo Dong Yang Ali Hatamizadeh An Xu Ziyue Xu ... F. Patella Elvira Stellato G. Carrafiello Vishal M. Patel H. Roth OOD FedML 28 32 0 12 Mar 2022
Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures Anshumali Shrivastava Zhao Song Zhaozhuo Xu 27 28 0 30 Nov 2021
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning Xiaoxiao Zhao Jinlong Lei Li Li Jie-bin Chen OffRL 20 2 0 25 Nov 2021
AGGLIO: Global Optimization for Locally Convex Functions Debojyoti Dey B. Mukhoty Purushottam Kar 16 2 0 06 Nov 2021
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima Zixiang Chen Dongruo Zhou Quanquan Gu 43 1 0 25 Oct 2021
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis Jikai Jin Samir Bhatt Haiyang Wang Liwei Wang 32 48 0 24 Oct 2021
Variance Reduction based Experience Replay for Policy Optimization Hua Zheng Wei Xie M. Feng OffRL 41 2 0 17 Oct 2021
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning Weichao Mao Lin F. Yang Kaipeng Zhang Tamer Bacsar 46 57 0 12 Oct 2021
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization Fu Wei Chenglong Bao Yang Liu 35 19 0 04 Oct 2021
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee Chunjiang Che Xiaoli Li Chuan Chen Xiaoyu He Zibin Zheng FedML 46 73 0 01 Aug 2021
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints Shaojie Li Yong Liu 26 13 0 19 Jul 2021
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent Spencer Frei Quanquan Gu 26 26 0 25 Jun 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
Decentralized Federated Averaging Tao Sun Dongsheng Li Bao Wang FedML 54 210 0 23 Apr 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 48 14 0 21 Mar 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark Schmidt Simon Lacoste-Julien 23 18 0 18 Feb 2021
Stochastic Gradient Langevin Dynamics with Variance Reduction Zhishen Huang Stephen Becker 17 7 0 12 Feb 2021
FedADC: Accelerated Federated Learning with Drift Control Emre Ozfatura Kerem Ozfatura Deniz Gunduz FedML 53 37 0 16 Dec 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 24 112 0 02 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 42 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 33 126 0 25 Aug 2020
AdaScale SGD: A User-Friendly Algorithm for Distributed Training Tyler B. Johnson Pulkit Agrawal Haijie Gu Carlos Guestrin ODL 30 37 0 09 Jul 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi Ma 53 23 0 18 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 35 23 0 31 May 2020
Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning Pengzhan Guo Zeyang Ye Keli Xiao Wei Zhu 24 14 0 07 Apr 2020
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions V. Patel 21 23 0 01 Apr 2020