Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness

4 December 2022

Papers citing "Convergence of ease-controlled Random Reshuffling gradient Algorithms under Lipschitz smoothness"

24 / 24 papers shown

Title
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression Abdurakhmon Sadiev Grigory Malinovsky Eduard A. Gorbunov Igor Sokolov Ahmed Khaled Konstantin Burlachenko Peter Richtárik FedML 51 21 0 14 Jun 2022
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization Grigory Malinovsky Konstantin Mishchenko Peter Richtárik FedML 46 24 0 26 Jan 2022
Convergence of Random Reshuffling Under The Kurdyka-Łojasiewicz Inequality Xiao Li Andre Milzarek Junwen Qiu 39 20 0 10 Oct 2021
Random Reshuffling with Variance Reduction: New Analysis and Better Rates Grigory Malinovsky Alibek Sailanbayev Peter Richtárik 41 20 0 19 Apr 2021
Proximal and Federated Random Reshuffling Konstantin Mishchenko Ahmed Khaled Peter Richtárik FedML 54 31 0 12 Feb 2021
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation Robert Mansel Gower Othmane Sebbouh Nicolas Loizou 81 75 0 18 Jun 2020
SGD with shuffling: optimal rates without component convexity and large epoch requirements Kwangjun Ahn Chulhee Yun S. Sra 49 66 0 12 Jun 2020
Random Reshuffling: Simple Analysis with Vast Improvements Konstantin Mishchenko Ahmed Khaled Peter Richtárik 62 131 0 10 Jun 2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods Lam M. Nguyen Quoc Tran-Dinh Dzung Phan Phuong Ha Nguyen Marten van Dijk 83 78 0 19 Feb 2020
Better Theory for SGD in the Nonconvex World Ahmed Khaled Peter Richtárik 61 183 0 09 Feb 2020
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions Yunwen Lei Ting Hu Guiying Li K. Tang MLT 77 118 0 03 Feb 2019
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity Hilal Asi John C. Duchi 120 125 0 12 Oct 2018
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes Rachel A. Ward Xiaoxia Wu Léon Bottou ODL 59 364 0 05 Jun 2018
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning Tianyi Chen G. Giannakis Tao Sun W. Yin 53 298 0 25 May 2018
Adaptive Sampling Strategies for Stochastic Optimization Raghu Bollapragada R. Byrd J. Nocedal 44 116 0 30 Oct 2017
Stochastic Methods for Composite and Weakly Convex Optimization Problems John C. Duchi Feng Ruan 32 127 0 24 Mar 2017
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 417 2,936 0 15 Sep 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 231 3,206 0 15 Jun 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.7K 150,006 0 22 Dec 2014
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 131 1,823 0 01 Jul 2014
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Saeed Ghadimi Guanghui Lan ODL 120 1,548 0 22 Sep 2013
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 314 1,245 0 10 Sep 2013
Practical recommendations for gradient-based training of deep architectures Yoshua Bengio 3DH ODL 187 2,197 0 24 Jun 2012
Hybrid Deterministic-Stochastic Methods for Data Fitting M. Friedlander Mark Schmidt 194 387 0 13 Apr 2011