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
Random-reshuffled SARAH does not need a full gradient computations Aleksandr Beznosikov Martin Takáč 31 7 0 26 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
Linear Speedup in Personalized Collaborative Learning El Mahdi Chayti Sai Praneeth Karimireddy Sebastian U. Stich Nicolas Flammarion Martin Jaggi FedML 21 13 0 10 Nov 2021
DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting Tianxiang Zhan Yuanpeng He Hanwen Li Fuyuan Xiao AI4TS 14 0 0 07 Nov 2021
AGGLIO: Global Optimization for Locally Convex Functions Debojyoti Dey B. Mukhoty Purushottam Kar 21 2 0 06 Nov 2021
Federated Expectation Maximization with heterogeneity mitigation and variance reduction Aymeric Dieuleveut G. Fort Eric Moulines Geneviève Robin FedML 35 5 0 03 Nov 2021
Federated Semi-Supervised Learning with Class Distribution Mismatch Zhiguo Wang Xintong Wang Ruoyu Sun Tsung-Hui Chang FedML 39 12 0 29 Oct 2021
Stochastic Primal-Dual Deep Unrolling Junqi Tang Subhadip Mukherjee Carola-Bibiane Schönlieb 24 4 0 19 Oct 2021
Nys-Newton: Nyström-Approximated Curvature for Stochastic Optimization Dinesh Singh Hardik Tankaria M. Yamada ODL 47 2 0 16 Oct 2021
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers Yujing Ma Florin Rusu Kesheng Wu A. Sim 48 3 0 13 Oct 2021
$$\bar{G}_{mst}$:An Unbiased Stratified Statistic and a Fast Gradient Optimization Algorithm Based on It$ $\bar{G}_{mst}$ :An Unbiased Stratified Statistic and a Fast Gradient Optimization Algorithm Based on It Aixiang Chen 24 0 0 07 Oct 2021
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization Kaiwen Zhou Anthony Man-Cho So James Cheng 29 1 0 30 Sep 2021
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features Bruce W. Lee Yoonna Jang J. Lee VLM 48 75 0 25 Sep 2021
Asynchronous Federated Learning on Heterogeneous Devices: A Survey Chenhao Xu Youyang Qu Yong Xiang Longxiang Gao FedML 104 246 0 09 Sep 2021
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization Manuel Madeira Renato M. P. Negrinho J. Xavier P. Aguiar 31 0 0 07 Sep 2021
L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method Bugra Can Saeed Soori M. Dehnavi Mert Gurbuzbalaban 50 2 0 20 Aug 2021
Construction Cost Index Forecasting: A Multi-feature Fusion Approach Tianxiang Zhan Yuanpeng He Fuyuan Xiao 45 0 0 18 Aug 2021
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning Charlie Hou K. K. Thekumparampil Giulia Fanti Sewoong Oh FedML 39 14 0 16 Aug 2021
Decentralized Composite Optimization with Compression Yao Li Xiaorui Liu Jiliang Tang Ming Yan Kun Yuan 29 9 0 10 Aug 2021
ErrorCompensatedX: error compensation for variance reduced algorithms Hanlin Tang Yao Li Ji Liu Ming Yan 32 10 0 04 Aug 2021
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control Xin-Yang Liu Jian-Xun Wang AI4CE 31 38 0 31 Jul 2021
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning Hua Huang Fanhua Shang Yuanyuan Liu Hongying Liu FedML 29 14 0 23 Jun 2021
Stochastic Polyak Stepsize with a Moving Target Robert Mansel Gower Aaron Defazio Michael G. Rabbat 32 17 0 22 Jun 2021
Decentralized Constrained Optimization: Double Averaging and Gradient Projection Firooz Shahriari-Mehr David Bosch Ashkan Panahi 21 8 0 21 Jun 2021
Secure Distributed Training at Scale Eduard A. Gorbunov Alexander Borzunov Michael Diskin Max Ryabinin FedML 26 15 0 21 Jun 2021
Asynchronous Distributed Optimization with Redundancy in Cost Functions Shuo Liu Nirupam Gupta Nitin H. Vaidya 31 3 0 07 Jun 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar Aleksander Madry FAtt 22 88 0 11 May 2021
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning Xin Zhang Jia Liu Zhengyuan Zhu Elizabeth S. Bentley 51 14 0 04 May 2021
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity Shaocong Ma Ziyi Chen Yi Zhou Shaofeng Zou 17 11 0 30 Mar 2021
ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method Zhize Li 48 14 0 21 Mar 2021
Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques Filip Hanzely Boxin Zhao Mladen Kolar FedML 37 53 0 19 Feb 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
Distributed Second Order Methods with Fast Rates and Compressed Communication Rustem Islamov Xun Qian Peter Richtárik 34 51 0 14 Feb 2021
Stochastic Gradient Langevin Dynamics with Variance Reduction Zhishen Huang Stephen Becker 17 7 0 12 Feb 2021
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate Haishan Ye Tong Zhang 21 25 0 08 Feb 2021
Urban land-use analysis using proximate sensing imagery: a survey Zhinan Qiao Xiaohui Yuan 19 18 0 13 Jan 2021
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization Karan N. Chadha Gary Cheng John C. Duchi 62 16 0 07 Jan 2021
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction Haishan Ye Wei Xiong Tong Zhang 16 16 0 30 Dec 2020
Optimising cost vs accuracy of decentralised analytics in fog computing environments Lorenzo Valerio A. Passarella M. Conti 35 1 0 09 Dec 2020
Faster Non-Convex Federated Learning via Global and Local Momentum Rudrajit Das Anish Acharya Abolfazl Hashemi Sujay Sanghavi Inderjit S. Dhillon Ufuk Topcu FedML 40 82 0 07 Dec 2020
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm G. Fort Eric Moulines Hoi-To Wai TPM 27 7 0 30 Nov 2020
SMG: A Shuffling Gradient-Based Method with Momentum Trang H. Tran Lam M. Nguyen Quoc Tran-Dinh 23 21 0 24 Nov 2020
A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free! D. Kovalev Anastasia Koloskova Martin Jaggi Peter Richtárik Sebastian U. Stich 31 73 0 03 Nov 2020
Local SGD: Unified Theory and New Efficient Methods Eduard A. Gorbunov Filip Hanzely Peter Richtárik FedML 37 109 0 03 Nov 2020
Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis Shaocong Ma Yi Zhou Shaofeng Zou OffRL 22 14 0 26 Oct 2020
AEGD: Adaptive Gradient Descent with Energy Hailiang Liu Xuping Tian ODL 27 11 0 10 Oct 2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction Wei Deng Qi Feng G. Karagiannis Guang Lin F. Liang 38 9 0 02 Oct 2020
Variance-Reduced Methods for Machine Learning Robert Mansel Gower Mark Schmidt Francis R. Bach Peter Richtárik 24 112 0 02 Oct 2020
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications F. Iutzeler J. Malick 12 16 0 02 Oct 2020
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning Guannan Liang Qianqian Tong Jiahao Ding Miao Pan J. Bi 27 0 0 14 Sep 2020