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
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization Luyao Guo Jinde Cao Xinli Shi Shaofu Yang 20 0 0 06 Dec 2022
On the effectiveness of partial variance reduction in federated learning with heterogeneous data Bo Li Mikkel N. Schmidt T. S. Alstrøm Sebastian U. Stich FedML 37 9 0 05 Dec 2022
Covariance Estimators for the ROOT-SGD Algorithm in Online Learning Yiling Luo X. Huo Y. Mei 8 2 0 02 Dec 2022
Stochastic Steffensen method Minda Zhao Zehua Lai Lek-Heng Lim ODL 15 3 0 28 Nov 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
Impact of Redundancy on Resilience in Distributed Optimization and Learning Shuo Liu Nirupam Gupta Nitin H. Vaidya 39 2 0 16 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 32 15 0 03 Nov 2022
Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$ -Means Clustering Lingxiao Huang Zhize Li Jialin Sun Haoyu Zhao FedML 49 9 0 26 Oct 2022
Block-wise Primal-dual Algorithms for Large-scale Doubly Penalized ANOVA Modeling Penghui Fu Z. Tan 21 5 0 20 Oct 2022
Unsupervised visualization of image datasets using contrastive learning Jan Boehm Philipp Berens D. Kobak SSL 31 15 0 18 Oct 2022
SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities Aleksandr Beznosikov Alexander Gasnikov 45 2 0 12 Oct 2022
Double Averaging and Gradient Projection: Convergence Guarantees for Decentralized Constrained Optimization Firooz Shahriari-Mehr Ashkan Panahi 19 1 0 06 Oct 2022
Smooth Monotone Stochastic Variational Inequalities and Saddle Point Problems: A Survey Aleksandr Beznosikov Boris Polyak Eduard A. Gorbunov D. Kovalev Alexander Gasnikov 44 31 0 29 Aug 2022
A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information Hardik Tankaria N. Yamashita 23 1 0 23 Aug 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 40 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 34 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 38 15 0 08 Jul 2022
MF-OMO: An Optimization Formulation of Mean-Field Games Xin Guo Anran Hu Junzi Zhang 50 14 0 20 Jun 2022
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms Lam M. Nguyen Trang H. Tran 34 2 0 13 Jun 2022
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 55 6 0 06 Jun 2022
Stochastic Gradient Methods with Preconditioned Updates Abdurakhmon Sadiev Aleksandr Beznosikov Abdulla Jasem Almansoori Dmitry Kamzolov R. Tappenden Martin Takáč ODL 39 9 0 01 Jun 2022
A principled framework for the design and analysis of token algorithms Hadrien Hendrikx FedML 29 13 0 30 May 2022
Confederated Learning: Federated Learning with Decentralized Edge Servers Bin Wang Jun Fang Hongbin Li Xiaojun Yuan Qing Ling FedML 28 23 0 30 May 2022
SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer Wei Zhang Yun-Jian Bao ODL 33 2 0 20 May 2022
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning M. Yousefi Angeles Martinez ODL 21 1 0 18 May 2022
Federated Random Reshuffling with Compression and Variance Reduction Grigory Malinovsky Peter Richtárik FedML 29 10 0 08 May 2022
Byzantine Fault Tolerance in Distributed Machine Learning : a Survey Djamila Bouhata Hamouma Moumen Moumen Hamouma Ahcène Bounceur AI4CE 31 7 0 05 May 2022
An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms Binghui Xie Chen Jin Kaiwen Zhou James Cheng Wei Meng 45 1 0 28 Apr 2022
Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection S. Shaw Keaton Chia J. Kleissl 16 1 0 28 Apr 2022
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning Afsaneh Mahmoudi H. S. Ghadikolaei José Hélio da Cruz Júnior Carlo Fischione 30 9 0 16 Apr 2022
An Adaptive Gradient Method with Energy and Momentum Hailiang Liu Xuping Tian ODL 21 9 0 23 Mar 2022
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation An Xu Wenqi Li Pengfei Guo Dong Yang H. Roth Ali Hatamizadeh Can Zhao Daguang Xu Heng-Chiao Huang Ziyue Xu FedML 38 52 0 18 Mar 2022
Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients Junqi Tang 31 2 0 14 Mar 2022
Data-Consistent Local Superresolution for Medical Imaging Junqi Tang SupR 33 0 0 22 Feb 2022
MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate Aixiang Chen Chen Jinting Zhang Zanbo Zhang Zhihong Li 48 0 0 21 Feb 2022
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Aleksandr Beznosikov Eduard A. Gorbunov Hugo Berard Nicolas Loizou 24 49 0 15 Feb 2022
Equivariance Regularization for Image Reconstruction Junqi Tang 32 2 0 10 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 39 15 0 05 Feb 2022
Decentralized Stochastic Variance Reduced Extragradient Method Luo Luo Haishan Ye 27 7 0 01 Feb 2022
L-SVRG and L-Katyusha with Adaptive Sampling Boxin Zhao Boxiang Lyu Mladen Kolar 26 3 0 31 Jan 2022
A dual approach for federated learning Zhenan Fan Huang Fang M. Friedlander FedML 18 3 0 26 Jan 2022
Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits Sunwoo Lee Anit Kumar Sahu Chaoyang He Salman Avestimehr FedML 33 17 0 11 Jan 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 44 10 0 28 Dec 2021
Accelerated and instance-optimal policy evaluation with linear function approximation Tianjiao Li Guanghui Lan A. Pananjady OffRL 44 13 0 24 Dec 2021
A Continuous-time Stochastic Gradient Descent Method for Continuous Data Kexin Jin J. Latz Chenguang Liu Carola-Bibiane Schönlieb 28 9 0 07 Dec 2021
Training Structured Neural Networks Through Manifold Identification and Variance Reduction Zih-Syuan Huang Ching-pei Lee AAML 53 9 0 05 Dec 2021
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