SGD and Hogwild! Convergence Without the Bounded Gradients Assumption

11 February 2018

Papers citing "SGD and Hogwild! Convergence Without the Bounded Gradients Assumption"

50 / 137 papers shown

Title
$γ$ -FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning Rongwei Lu Yutong Jiang Jinrui Zhang Chunyang Li Yifei Zhu Bin Chen Zhi Wang FedML 7 0 0 18 May 2025
Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Ruinan Jin Difei Cheng Hong Qiao Xin Shi Shaodong Liu Bo Zhang 43 0 0 17 Apr 2025
Biased Federated Learning under Wireless Heterogeneity Muhammad Faraz Ul Abrar Nicolò Michelusi FedML 49 0 0 08 Mar 2025
Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity A. Maranjyan A. Tyurin Peter Richtárik 44 3 0 28 Jan 2025
PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy Linh Tran Timothy Castiglia Stacy Patterson Ana Milanova FedML 50 0 0 23 Jan 2025
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis Ruichen Luo Sebastian U Stich Samuel Horváth Martin Takáč 43 0 0 08 Jan 2025
Beyond adaptive gradient: Fast-Controlled Minibatch Algorithm for large-scale optimization Corrado Coppola Lorenzo Papa Irene Amerini L. Palagi ODL 84 0 0 24 Nov 2024
Equitable Federated Learning with Activation Clustering Antesh Upadhyay Abolfazl Hashemi FedML 26 0 0 24 Oct 2024
An Attention-Based Algorithm for Gravity Adaptation Zone Calibration Chen Yu 24 0 0 06 Oct 2024
MindFlayer: Efficient Asynchronous Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times A. Maranjyan Omar Shaikh Omar Peter Richtárik 31 3 0 05 Oct 2024
On the SAGA algorithm with decreasing step Luis Fredes Bernard Bercu Eméric Gbaguidi 29 1 0 02 Oct 2024
FADAS: Towards Federated Adaptive Asynchronous Optimization Yujia Wang Shiqiang Wang Songtao Lu Jinghui Chen FedML 42 3 0 25 Jul 2024
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance Dimitris Oikonomou Nicolas Loizou 55 4 0 06 Jun 2024
Demystifying SGD with Doubly Stochastic Gradients Kyurae Kim Joohwan Ko Yian Ma Jacob R. Gardner 53 0 0 03 Jun 2024
Local Methods with Adaptivity via Scaling Saveliy Chezhegov Sergey Skorik Nikolas Khachaturov Danil Shalagin A. Avetisyan Aleksandr Beznosikov Martin Takáč Yaroslav Kholodov Alexander Gasnikov 61 2 0 02 Jun 2024
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data Ziqing Fan Ruipeng Zhang Jiangchao Yao Bo Han Ya Zhang Yanfeng Wang FedML 40 12 0 29 May 2024
Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations A. Tyurin Kaja Gruntkowska Peter Richtárik 44 3 0 24 May 2024
Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approach Hoang Huy Nguyen Yan Li Tuo Zhao 29 1 0 03 Apr 2024
On the Last-Iterate Convergence of Shuffling Gradient Methods Zijian Liu Zhengyuan Zhou 42 2 0 12 Mar 2024
Shuffling Momentum Gradient Algorithm for Convex Optimization Trang H. Tran Quoc Tran-Dinh Lam M. Nguyen 32 1 0 05 Mar 2024
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad Sayantan Choudhury N. Tupitsa Nicolas Loizou Samuel Horváth Martin Takáč Eduard A. Gorbunov 35 1 0 05 Mar 2024
On the Convergence of Federated Learning Algorithms without Data Similarity Ali Beikmohammadi Sarit Khirirat Sindri Magnússon FedML 41 1 0 29 Feb 2024
Provably Scalable Black-Box Variational Inference with Structured Variational Families Joohwan Ko Kyurae Kim W. Kim Jacob R. Gardner BDL 33 2 0 19 Jan 2024
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization Junwen Qiu Xiao Li Andre Milzarek 39 2 0 02 Dec 2023
On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations Antoine Godichon-Baggioni Nicklas Werge ODL 40 3 0 29 Nov 2023
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent Frederik Köhne Leonie Kreis Anton Schiela Roland A. Herzog 24 1 0 28 Nov 2023
Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR) Guo-qing Jiang Jinlong Liu Zixiang Ding Lin Guo W. Lin AI4CE 26 1 0 24 Sep 2023
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise Antesh Upadhyay Abolfazl Hashemi 44 9 0 14 Jul 2023
Towards a Better Theoretical Understanding of Independent Subnetwork Training Egor Shulgin Peter Richtárik AI4CE 34 6 0 28 Jun 2023
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths Charles Guille-Escuret Hiroki Naganuma Kilian Fatras Ioannis Mitliagkas 21 3 0 20 Jun 2023
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality Kejie Tang Weidong Liu Yichen Zhang Xi Chen 21 2 0 28 May 2023
On the Convergence of Black-Box Variational Inference Kyurae Kim Jisu Oh Kaiwen Wu Yi Ma Jacob R. Gardner BDL 48 15 0 24 May 2023
Breaking the Curse of Quality Saturation with User-Centric Ranking Zhuokai Zhao Yang Yang Wenyu Wang Chi-Yu Liu Yunluo Shi Wenjie Hu Haotian Zhang Shuangjun Yang 48 3 0 24 May 2023
Layer-wise Adaptive Step-Sizes for Stochastic First-Order Methods for Deep Learning Achraf Bahamou D. Goldfarb ODL 36 0 0 23 May 2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference Kyurae Kim Kaiwen Wu Jisu Oh Jacob R. Gardner BDL 31 7 0 18 Mar 2023
Considerations on the Theory of Training Models with Differential Privacy Marten van Dijk Phuong Ha Nguyen FedML 31 2 0 08 Mar 2023
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning Jianing Zhu Jiangchao Yao Tongliang Liu Quanming Yao Jianliang Xu Bo Han FedML 40 5 0 01 Mar 2023
Maximum Likelihood With a Time Varying Parameter Alberto Lanconelli Christopher S. A. Lauria 16 3 0 28 Feb 2023
Generalizing DP-SGD with Shuffling and Batch Clipping Marten van Dijk Phuong Ha Nguyen Toan N. Nguyen Lam M. Nguyen 20 1 0 12 Dec 2022
A note on diffusion limits for stochastic gradient descent Alberto Lanconelli Christopher S. A. Lauria DiffM 22 1 0 20 Oct 2022
Quantization for decentralized learning under subspace constraints Roula Nassif Stefan Vlaski Marco Carpentiero Vincenzo Matta Marc Antonini Ali H. Sayed 30 29 0 16 Sep 2022
Convergence of Batch Updating Methods with Approximate Gradients and/or Noisy Measurements: Theory and Computational Results Tadipatri Uday M. Vidyasagar 28 0 0 12 Sep 2022
Flexible Vertical Federated Learning with Heterogeneous Parties Timothy Castiglia Shiqiang Wang S. Patterson FedML 42 34 0 26 Aug 2022
Adaptive Learning Rates for Faster Stochastic Gradient Methods Samuel Horváth Konstantin Mishchenko Peter Richtárik ODL 41 7 0 10 Aug 2022
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design Minsu Kim Walid Saad Mohammad Mozaffari Merouane Debbah FedML MQ 23 27 0 19 Jul 2022
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates Yann Fraboni Richard Vidal Laetitia Kameni Marco Lorenzi FedML 27 24 0 21 Jun 2022
Finding Optimal Policy for Queueing Models: New Parameterization Trang H. Tran Lam M. Nguyen K. Scheinberg OffRL 23 2 0 21 Jun 2022
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data Timothy Castiglia Anirban Das Shiqiang Wang S. Patterson FedML 27 48 0 16 Jun 2022
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Anastasia Koloskova Sebastian U. Stich Martin Jaggi FedML 30 79 0 16 Jun 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim Jisu Oh Jacob R. Gardner Adji Bousso Dieng Hongseok Kim BDL 37 8 0 13 Jun 2022