SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

1 July 2014

Papers citing "SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives"

50 / 353 papers shown

Title
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
Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization Tianyi Chen Yuejiao Sun W. Yin 48 81 0 25 Aug 2020
Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning Shuang Qiu Zhuoran Yang Xiaohan Wei Jieping Ye Zhaoran Wang 33 38 0 23 Aug 2020
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning Bin Gu An Xu Zhouyuan Huo Cheng Deng Heng-Chiao Huang FedML 38 28 0 14 Aug 2020
A Survey on Large-scale Machine Learning Meng Wang Weijie Fu Xiangnan He Shijie Hao Xindong Wu 25 110 0 10 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 27 3 0 25 Jul 2020
On stochastic mirror descent with interacting particles: convergence properties and variance reduction Anastasia Borovykh N. Kantas P. Parpas G. Pavliotis 35 12 0 15 Jul 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
Stochastic Hamiltonian Gradient Methods for Smooth Games Nicolas Loizou Hugo Berard Alexia Jolicoeur-Martineau Pascal Vincent Simon Lacoste-Julien Ioannis Mitliagkas 41 50 0 08 Jul 2020
Stochastic Stein Discrepancies Jackson Gorham Anant Raj Lester W. Mackey 32 37 0 06 Jul 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi Ma 53 23 0 18 Jun 2020
Minibatch vs Local SGD for Heterogeneous Distributed Learning Blake E. Woodworth Kumar Kshitij Patel Nathan Srebro FedML 22 199 0 08 Jun 2020
Federated Stochastic Gradient Langevin Dynamics Khaoula El Mekkaoui Diego Mesquita P. Blomstedt Samuel Kaski FedML 37 24 0 23 Apr 2020
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration Wenlong Mou C. J. Li Martin J. Wainwright Peter L. Bartlett Michael I. Jordan 33 75 0 09 Apr 2020
Block Layer Decomposition schemes for training Deep Neural Networks L. Palagi R. Seccia 33 5 0 18 Mar 2020
Adaptive Federated Optimization Sashank J. Reddi Zachary B. Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Konecný Sanjiv Kumar H. B. McMahan FedML 58 1,395 0 29 Feb 2020
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets Ilqar Ramazanli Han Nguyen Hai Pham Sashank J. Reddi Barnabás Póczós 23 11 0 20 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
A Newton Frank-Wolfe Method for Constrained Self-Concordant Minimization Deyi Liu V. Cevher Quoc Tran-Dinh 38 15 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 40 17 0 11 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 13 180 0 09 Feb 2020
Adaptive Stochastic Optimization Frank E. Curtis K. Scheinberg ODL 19 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 44 152 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
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 30 168 0 19 Dec 2019
Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more Julien Mairal 24 22 0 17 Dec 2019
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods Belhal Karimi Hoi-To Wai Eric Moulines M. Lavielle 32 27 0 28 Oct 2019
Differentiable Convex Optimization Layers Akshay Agrawal Brandon Amos Shane T. Barratt Stephen P. Boyd Steven Diamond Zico Kolter 50 640 0 28 Oct 2019
Katyusha Acceleration for Convex Finite-Sum Compositional Optimization Yibo Xu Yangyang Xu 87 13 0 24 Oct 2019
The Practicality of Stochastic Optimization in Imaging Inverse Problems Junqi Tang K. Egiazarian Mohammad Golbabaee Mike Davies 27 30 0 22 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
Aggregated Gradient Langevin Dynamics Chao Zhang Jiahao Xie Zebang Shen P. Zhao Tengfei Zhou Hui Qian 33 1 0 21 Oct 2019
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme Tao Sun Yuejiao Sun Dongsheng Li Qing Liao 35 16 0 11 Oct 2019
Variance-Reduced Decentralized Stochastic Optimization with Gradient Tracking -- Part II: GT-SVRG Ran Xin U. Khan S. Kar 22 8 0 08 Oct 2019
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction Pan Xu F. Gao Quanquan Gu 33 83 0 18 Sep 2019
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods Ching-An Cheng Xinyan Yan Byron Boots 30 22 0 08 Aug 2019
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions Matthew Faw Rajat Sen Karthikeyan Shanmugam Constantine Caramanis Sanjay Shakkottai 36 3 0 23 Jul 2019
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization Adithya M. Devraj Jianshu Chen 30 13 0 22 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
Learning Activation Functions: A new paradigm for understanding Neural Networks Mohit Goyal R. Goyal Brejesh Lall 33 64 0 23 Jun 2019
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 25 3 0 22 Jun 2019
Reducing the variance in online optimization by transporting past gradients Sébastien M. R. Arnold Pierre-Antoine Manzagol Reza Babanezhad Ioannis Mitliagkas Nicolas Le Roux 26 28 0 08 Jun 2019
Global Optimality Guarantees For Policy Gradient Methods Jalaj Bhandari Daniel Russo 39 186 0 05 Jun 2019
Why gradient clipping accelerates training: A theoretical justification for adaptivity J.N. Zhang Tianxing He S. Sra Ali Jadbabaie 30 446 0 28 May 2019
Natural Compression for Distributed Deep Learning Samuel Horváth Chen-Yu Ho L. Horvath Atal Narayan Sahu Marco Canini Peter Richtárik 21 151 0 27 May 2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates Sharan Vaswani Aaron Mishkin I. Laradji Mark Schmidt Gauthier Gidel Simon Lacoste-Julien ODL 50 205 0 24 May 2019