Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems

10 July 2014

Papers citing "Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems"

39 / 39 papers shown

Title
A Coefficient Makes SVRG Effective Yida Yin Zhiqiu Xu Zhiyuan Li Trevor Darrell Zhuang Liu 44 1 0 09 Nov 2023
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
Federated Random Reshuffling with Compression and Variance Reduction Grigory Malinovsky Peter Richtárik FedML 29 10 0 08 May 2022
L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method Bugra Can Saeed Soori M. Dehnavi Mert Gurbuzbalaban 45 2 0 20 Aug 2021
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 42 0 0 26 Aug 2020
Federated Stochastic Gradient Langevin Dynamics Khaoula El Mekkaoui Diego Mesquita P. Blomstedt Samuel Kaski FedML 37 24 0 23 Apr 2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods Lam M. Nguyen Quoc Tran-Dinh Dzung Phan Phuong Ha Nguyen Marten van Dijk 39 78 0 19 Feb 2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization Samuel Horváth Lihua Lei Peter Richtárik Michael I. Jordan 57 30 0 13 Feb 2020
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems Filip Hanzely D. Kovalev Peter Richtárik 40 17 0 11 Feb 2020
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization Quoc Tran-Dinh Nhan H. Pham T. Dzung Lam M. Nguyen 27 49 0 08 Jul 2019
A Unifying Framework for Variance Reduction Algorithms for Finding Zeroes of Monotone Operators Xun Zhang W. Haskell Z. Ye 25 3 0 22 Jun 2019
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods Martin Morin Pontus Giselsson 20 2 0 21 Mar 2019
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise A. Kulunchakov Julien Mairal 34 44 0 25 Jan 2019
Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop D. Kovalev Samuel Horváth Peter Richtárik 36 155 0 24 Jan 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning Aaron Defazio Léon Bottou UQCV DRL 23 112 0 11 Dec 2018
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches Jie Liu Yu Rong Martin Takáč Junzhou Huang ODL 38 7 0 14 Jul 2018
A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm Konstantin Mishchenko F. Iutzeler J. Malick 19 22 0 25 Jun 2018
Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou Pan Xu Quanquan Gu 25 146 0 20 Jun 2018
Stochastic Variance-Reduced Policy Gradient Matteo Papini Damiano Binaghi Giuseppe Canonaco Matteo Pirotta Marcello Restelli 19 174 0 14 Jun 2018
Analysis of Biased Stochastic Gradient Descent Using Sequential Semidefinite Programs Bin Hu Peter M. Seiler Laurent Lessard 24 39 0 03 Nov 2017
Variance-Reduced Stochastic Learning under Random Reshuffling Bicheng Ying Kun Yuan Ali H. Sayed 31 13 0 04 Aug 2017
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints Bin Hu Peter M. Seiler Anders Rantzer 30 35 0 25 Jun 2017
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method Mark Eisen Aryan Mokhtari Alejandro Ribeiro 35 16 0 22 May 2017
Stochastic Recursive Gradient Algorithm for Nonconvex Optimization Lam M. Nguyen Jie Liu K. Scheinberg Martin Takáč 11 94 0 20 May 2017
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate Aryan Mokhtari Mert Gurbuzbalaban Alejandro Ribeiro 37 36 0 01 Nov 2016
Big Batch SGD: Automated Inference using Adaptive Batch Sizes Soham De A. Yadav David Jacobs Tom Goldstein ODL 37 62 0 18 Oct 2016
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure A. Bietti Julien Mairal 47 36 0 04 Oct 2016
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration Hongzhou Lin Julien Mairal Zaïd Harchaoui 33 13 0 04 Oct 2016
Trading-off variance and complexity in stochastic gradient descent Vatsal Shah Megasthenis Asteris Anastasios Kyrillidis Sujay Sanghavi 25 13 0 22 Mar 2016
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods Zeyuan Allen-Zhu ODL 35 577 0 18 Mar 2016
Variance Reduction for Faster Non-Convex Optimization Zeyuan Allen-Zhu Elad Hazan ODL 32 390 0 17 Mar 2016
A Simple Practical Accelerated Method for Finite Sums Aaron Defazio 30 121 0 08 Feb 2016
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters Zeyuan Allen-Zhu Yang Yuan Karthik Sridharan 20 27 0 05 Feb 2016
New Optimisation Methods for Machine Learning Aaron Defazio 46 6 0 09 Oct 2015
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants Sashank J. Reddi Ahmed S. Hefny S. Sra Barnabás Póczós Alex Smola 40 194 0 23 Jun 2015
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives Zeyuan Allen-Zhu Yang Yuan 31 195 0 05 Jun 2015
SDCA without Duality Shai Shalev-Shwartz 27 47 0 22 Feb 2015
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning Julien Mairal 79 317 0 18 Feb 2014
Minimizing Finite Sums with the Stochastic Average Gradient Mark Schmidt Nicolas Le Roux Francis R. Bach 114 1,244 0 10 Sep 2013