Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization

Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization

5 July 2016

Papers citing "Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization"

17 / 17 papers shown

Title
Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization Aditya Ranganath Mukesh Singhal Roummel Marcia ODL 65 0 0 17 Feb 2025
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods Yanli Liu Kaipeng Zhang Tamer Basar W. Yin 42 102 0 15 Nov 2022
SP2: A Second Order Stochastic Polyak Method Shuang Li W. Swartworth Martin Takávc Deanna Needell Robert Mansel Gower 23 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 52 5 0 06 Jun 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
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization Fu Wei Chenglong Bao Yang Liu 24 19 0 04 Oct 2021
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks Elizabeth Newman Julianne Chung Matthias Chung Lars Ruthotto 47 6 0 28 Sep 2021
Differentially private inference via noisy optimization Marco Avella-Medina Casey Bradshaw Po-Ling Loh FedML 40 29 0 19 Mar 2021
Learning explanations that are hard to vary Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele Bernhard Schölkopf FAtt 21 178 0 01 Sep 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters Filip Hanzely 32 0 0 26 Aug 2020
Learning Game-Theoretic Models of Multiagent Trajectories Using Implicit Layers Philipp Geiger C. Straehle AI4CE 33 26 0 17 Aug 2020
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size Ke Ma Jinshan Zeng Qianqian Xu Xiaochun Cao Wei Liu Yuan Yao 20 3 0 01 Dec 2019
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization Seonho Park Seung Hyun Jung P. Pardalos ODL 21 15 0 27 Jun 2019
A Linearly-Convergent Stochastic L-BFGS Algorithm Philipp Moritz Robert Nishihara Michael I. Jordan ODL 29 232 0 09 Aug 2015
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 90 736 0 19 Mar 2014
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 101 570 0 08 Dec 2012