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
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time Y. Lee Zhao Song Qiuyi Zhang 24 115 0 11 May 2019
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization Baojian Zhou F. Chen Yiming Ying 34 7 0 09 May 2019
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization Rong Ge Zhize Li Weiyao Wang Xiang Wang 19 34 0 01 May 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient Tatjana Chavdarova Gauthier Gidel François Fleuret Simon Lacoste-Julien 25 135 0 18 Apr 2019
On the Adaptivity of Stochastic Gradient-Based Optimization Lihua Lei Michael I. Jordan ODL 24 22 0 09 Apr 2019
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 20 2 0 21 Mar 2019
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise Yeming Wen Kevin Luk Maxime Gazeau Guodong Zhang Harris Chan Jimmy Ba ODL 25 22 0 21 Feb 2019
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization Feihu Huang Bin Gu Zhouyuan Huo Songcan Chen Heng-Chiao Huang 14 26 0 16 Feb 2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization Nhan H. Pham Lam M. Nguyen Dzung Phan Quoc Tran-Dinh 18 139 0 15 Feb 2019
Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample A. Berahas Majid Jahani Peter Richtárik Martin Takávc 24 40 0 28 Jan 2019
Asynchronous Accelerated Proximal Stochastic Gradient for Strongly Convex Distributed Finite Sums Hadrien Hendrikx Francis R. Bach Laurent Massoulié FedML 21 26 0 28 Jan 2019
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it Konstantin Mishchenko Filip Hanzely Peter Richtárik 16 13 0 27 Jan 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 34 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
SAGA with Arbitrary Sampling Xun Qian Zheng Qu Peter Richtárik 37 25 0 24 Jan 2019
SGD Converges to Global Minimum in Deep Learning via Star-convex Path Yi Zhou Junjie Yang Huishuai Zhang Yingbin Liang Vahid Tarokh 22 71 0 02 Jan 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio Léon Bottou UQCV DRL 23 112 0 11 Dec 2018
Asynchronous Stochastic Composition Optimization with Variance Reduction Shuheng Shen Linli Xu Jingchang Liu Junliang Guo Qing Ling 27 2 0 15 Nov 2018
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate J.N. Zhang Hongyi Zhang S. Sra 26 39 0 10 Nov 2018
New Convergence Aspects of Stochastic Gradient Algorithms Lam M. Nguyen Phuong Ha Nguyen Peter Richtárik K. Scheinberg Martin Takáč Marten van Dijk 33 66 0 10 Nov 2018
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy Majid Jahani Xi He Chenxin Ma Aryan Mokhtari Dheevatsa Mudigere Alejandro Ribeiro Martin Takáč 30 18 0 26 Oct 2018
SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms Zhe Wang Kaiyi Ji Yi Zhou Yingbin Liang Vahid Tarokh ODL 35 81 0 25 Oct 2018
Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates Lucas Cassano Kun Yuan Ali H. Sayed 22 39 0 17 Oct 2018
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron Sharan Vaswani Francis R. Bach Mark Schmidt 30 296 0 16 Oct 2018
Quasi-hyperbolic momentum and Adam for deep learning Jerry Ma Denis Yarats ODL 84 129 0 16 Oct 2018
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD Marten van Dijk Lam M. Nguyen Phuong Ha Nguyen Dzung Phan 36 6 0 09 Oct 2018
POLO: a POLicy-based Optimization library Arda Aytekin Martin Biel M. Johansson 25 3 0 08 Oct 2018
Accelerating Stochastic Gradient Descent Using Antithetic Sampling Jingchang Liu Linli Xu 19 2 0 07 Oct 2018
Continuous-time Models for Stochastic Optimization Algorithms Antonio Orvieto Aurelien Lucchi 19 31 0 05 Oct 2018
A fast quasi-Newton-type method for large-scale stochastic optimisation A. Wills Carl Jidling Thomas B. Schon ODL 36 7 0 29 Sep 2018
Optimal Matrix Momentum Stochastic Approximation and Applications to Q-learning Adithya M. Devraj Ana Bušić Sean P. Meyn 22 4 0 17 Sep 2018
SEGA: Variance Reduction via Gradient Sketching Filip Hanzely Konstantin Mishchenko Peter Richtárik 25 71 0 09 Sep 2018
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches Jie Liu Yu Rong Martin Takáč Junzhou Huang ODL 38 7 0 14 Jul 2018
Dual optimization for convex constrained objectives without the gradient-Lipschitz assumption Martin Bompaire Emmanuel Bacry Stéphane Gaïffas 30 6 0 10 Jul 2018
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator Cong Fang C. J. Li Zhouchen Lin Tong Zhang 50 571 0 04 Jul 2018
Quasi-Monte Carlo Variational Inference Alexander K. Buchholz F. Wenzel Stephan Mandt BDL 30 58 0 04 Jul 2018
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates Kaiwen Zhou Fanhua Shang James Cheng 24 74 0 28 Jun 2018
A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm Konstantin Mishchenko F. Iutzeler J. Malick 21 22 0 25 Jun 2018
Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou Pan Xu Quanquan Gu 25 146 0 20 Jun 2018
Laplacian Smoothing Gradient Descent Stanley Osher Bao Wang Penghang Yin Xiyang Luo Farzin Barekat Minh Pham A. Lin ODL 22 43 0 17 Jun 2018
Stochastic Variance-Reduced Policy Gradient Matteo Papini Damiano Binaghi Giuseppe Canonaco Matteo Pirotta Marcello Restelli 21 174 0 14 Jun 2018
Towards Riemannian Accelerated Gradient Methods Hongyi Zhang S. Sra 21 53 0 07 Jun 2018
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $β$ -Divergences Jeremias Knoblauch Jack Jewson Theodoros Damoulas 25 56 0 06 Jun 2018
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization Hoi-To Wai Zhuoran Yang Zhaoran Wang Mingyi Hong 30 169 0 03 Jun 2018
Nonlinear Acceleration of CNNs Damien Scieur Edouard Oyallon Alexandre d’Aspremont Francis R. Bach 23 11 0 01 Jun 2018
Stochastic algorithms with descent guarantees for ICA Pierre Ablin Alexandre Gramfort J. Cardoso Francis R. Bach CML 18 7 0 25 May 2018
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication Zebang Shen Aryan Mokhtari Tengfei Zhou P. Zhao Hui Qian 30 56 0 25 May 2018
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning Tianyi Chen G. Giannakis Tao Sun W. Yin 34 297 0 25 May 2018
D $^2$ : Decentralized Training over Decentralized Data Hanlin Tang Xiangru Lian Ming Yan Ce Zhang Ji Liu 20 348 0 19 Mar 2018
Constrained Deep Learning using Conditional Gradient and Applications in Computer Vision Sathya Ravi Tuan Dinh Vishnu Suresh Lokhande Vikas Singh AI4CE 33 22 0 17 Mar 2018