Stochastic Gradient Langevin Dynamics with Variance Reduction

12 February 2021

Papers citing "Stochastic Gradient Langevin Dynamics with Variance Reduction"

19 / 19 papers shown

Title
Replica Exchange for Non-Convex Optimization Jing-rong Dong Xin T. Tong 65 21 0 23 Jan 2020
Efficiently avoiding saddle points with zero order methods: No gradients required Lampros Flokas Emmanouil-Vasileios Vlatakis-Gkaragkounis Georgios Piliouras 60 34 0 29 Oct 2019
Heavy-ball Algorithms Always Escape Saddle Points Tao Sun Dongsheng Li Zhe Quan Hao Jiang Shengguo Li Y. Dou ODL 52 21 0 23 Jul 2019
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics Xi Chen S. Du Xin T. Tong 61 33 0 30 Apr 2019
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points Zhize Li 67 38 0 19 Apr 2019
Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions Zhishen Huang Stephen Becker 24 8 0 24 Jan 2019
Escaping Saddle Points in Constrained Optimization Aryan Mokhtari Asuman Ozdaglar Ali Jadbabaie 63 53 0 06 Sep 2018
An Alternative View: When Does SGD Escape Local Minima? Robert D. Kleinberg Yuanzhi Li Yang Yuan MLT 77 317 0 17 Feb 2018
User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient A. Dalalyan Avetik G. Karagulyan 75 297 0 29 Sep 2017
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization Pan Xu Jinghui Chen Difan Zou Quanquan Gu 81 205 0 20 Jul 2017
Convergence of Langevin MCMC in KL-divergence Xiang Cheng Peter L. Bartlett 58 194 0 25 May 2017
How to Escape Saddle Points Efficiently Chi Jin Rong Ge Praneeth Netrapalli Sham Kakade Michael I. Jordan ODL 229 836 0 02 Mar 2017
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics Yuchen Zhang Percy Liang Moses Charikar 67 236 0 18 Feb 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis Maxim Raginsky Alexander Rakhlin Matus Telgarsky 73 521 0 13 Feb 2017
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 280 1,220 0 16 Aug 2016
Stochastic Variance Reduction for Nonconvex Optimization Sashank J. Reddi Ahmed S. Hefny S. Sra Barnabás Póczós Alex Smola 101 604 0 19 Mar 2016
Variance Reduction for Faster Non-Convex Optimization Zeyuan Allen-Zhu Elad Hazan ODL 118 392 0 17 Mar 2016
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition Rong Ge Furong Huang Chi Jin Yang Yuan 140 1,059 0 06 Mar 2015
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio Francis R. Bach Simon Lacoste-Julien ODL 135 1,828 0 01 Jul 2014