A Linearly-Convergent Stochastic L-BFGS Algorithm

9 August 2015

Papers citing "A Linearly-Convergent Stochastic L-BFGS Algorithm"

30 / 30 papers shown

Title
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning Jingruo Sun Zachary Frangella Madeleine Udell 36 0 0 28 Jan 2025
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients Sachin Garg A. Berahas Michal Dereziñski 46 1 0 23 Apr 2024
Online Learning Under A Separable Stochastic Approximation Framework Min Gan Xiang-Xiang Su Guang-yong Chen Jing Chen 28 0 0 12 May 2023
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization G. Fort Eric Moulines 46 6 0 02 Jan 2023
Stochastic Steffensen method Minda Zhao Zehua Lai Lek-Heng Lim ODL 15 3 0 28 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
A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information Hardik Tankaria N. Yamashita 13 1 0 23 Aug 2022
SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization Pourya Behmandpoor P. Latafat Andreas Themelis Marc Moonen Panagiotis Patrinos 29 2 0 17 Jul 2022
SP2: A Second Order Stochastic Polyak Method Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 26 13 0 17 Jul 2022
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm Xinteng Ma Renyi Bao Jinpeng Jiang Yang Liu Arthur Jiang Junhua Yan Xin Liu Zhisong Pan FedML 32 6 0 20 Jun 2022
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches Michal Derezinski 55 5 0 06 Jun 2022
Estimation and Inference by Stochastic Optimization Jean-Jacques Forneron 35 5 0 06 May 2022
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part I Jiaojiao Zhang Huikang Liu Anthony Man-Cho So Qing Ling 24 14 0 19 Jan 2022
Nys-Newton: Nyström-Approximated Curvature for Stochastic Optimization Dinesh Singh Hardik Tankaria M. Yamada ODL 42 2 0 16 Oct 2021
Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm Lucas N. Egidio A. Hansson B. Wahlberg 19 12 0 03 Oct 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 37 0 0 26 Aug 2020
Training (Overparametrized) Neural Networks in Near-Linear Time Jan van den Brand Binghui Peng Zhao Song Omri Weinstein ODL 29 82 0 20 Jun 2020
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training Diego Granziol S. Zohren Stephen J. Roberts ODL 37 49 0 16 Jun 2020
Practical Quasi-Newton Methods for Training Deep Neural Networks D. Goldfarb Yi Ren Achraf Bahamou ODL 10 104 0 16 Jun 2020
Communication-Efficient Edge AI: Algorithms and Systems Yuanming Shi Kai Yang Tao Jiang Jun Zhang Khaled B. Letaief GNN 29 326 0 22 Feb 2020
A fast quasi-Newton-type method for large-scale stochastic optimisation A. Wills Carl Jidling Thomas B. Schon ODL 31 7 0 29 Sep 2018
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis Tal Ben-Nun Torsten Hoefler GNN 33 703 0 26 Feb 2018
An inexact subsampled proximal Newton-type method for large-scale machine learning Xuanqing Liu Cho-Jui Hsieh J. Lee Yuekai Sun 32 15 0 28 Aug 2017
Diving into the shallows: a computational perspective on large-scale shallow learning Siyuan Ma M. Belkin 32 76 0 30 Mar 2017
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 69 1,878 0 08 Oct 2016
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration Hongzhou Lin Julien Mairal Zaïd Harchaoui 31 13 0 04 Oct 2016
Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization Tianlin Li Shiqian Ma D. Goldfarb Wei Liu 24 177 0 05 Jul 2016
Predictive Coarse-Graining M. Schöberl N. Zabaras P. Koutsourelakis 17 34 0 26 May 2016
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari Alejandro Ribeiro ODL 17 32 0 24 May 2016
Kalman-based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning V. Patel 23 35 0 03 Dec 2015