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"

24 / 24 papers shown

Title
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization Wei Zhang Arif Hassan Zidan Afrar Jahin Wei Zhang Tianming Liu ODL 50 0 0 16 May 2025
Personalized Federated Learning under Model Dissimilarity Constraints Samuel Erickson Mikael Johansson FedML 194 0 0 12 May 2025
Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL Jiarui Yao Yifan Hao Hanning Zhang Hanze Dong Wei Xiong Nan Jiang Tong Zhang LRM 92 1 0 05 May 2025
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis Ruichen Luo Sebastian U Stich Samuel Horváth Martin Takáč 73 0 0 08 Jan 2025
Efficient Optimization Algorithms for Linear Adversarial Training Antônio H. Ribeiro Thomas B. Schon Dave Zahariah Francis Bach AAML 70 1 0 16 Oct 2024
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold Hoang Phuc Hau Luu Hanlin Yu Bernardo Williams Marcelo Hartmann Arto Klami DRL 106 0 0 03 Oct 2024
Ordered Momentum for Asynchronous SGD Chang-Wei Shi Yi-Rui Yang Wu-Jun Li ODL 123 0 0 27 Jul 2024
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization M. D. Santis Jordan Frécon Francesco Rinaldi Saverio Salzo Martin Schmidt Martin Schmidt 73 0 0 21 Aug 2023
Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective Adam N. Elmachtoub Henry Lam Haofeng Zhang Yunfan Zhao 118 6 0 13 Apr 2023
Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems Quoc Tran-Dinh Yang Luo 148 8 0 08 Jan 2023
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 69 6 0 06 Jun 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Qing Cui Jun Zhou Chaochao Chen FedML 100 11 0 28 Dec 2021
Push-SAGA: A decentralized stochastic algorithm with variance reduction over directed graphs Muhammad I. Qureshi Ran Xin S. Kar U. Khan 64 20 0 13 Aug 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song Yong Jiang Yi-An Ma 86 23 0 18 Jun 2020
Parallel Streaming Wasserstein Barycenters Matthew Staib Sebastian Claici Justin Solomon Stefanie Jegelka 51 89 0 21 May 2017
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč ODL 157 603 0 01 Mar 2017
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport Hiroyuki Sato Hiroyuki Kasai Bamdev Mishra 114 58 0 18 Feb 2017
Importance Sampling for Minibatches Dominik Csiba Peter Richtárik 74 115 0 06 Feb 2016
Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems Aaron Defazio T. Caetano Justin Domke 102 169 0 10 Jul 2014
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 147 738 0 19 Mar 2014
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 138 318 0 18 Feb 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 289 1,246 0 10 Sep 2013
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang ODL 91 463 0 10 Sep 2013
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 153 1,032 0 10 Sep 2012