Reducing the variance in online optimization by transporting past
gradients

Reducing the variance in online optimization by transporting past gradients

8 June 2019

Sébastien M. R. Arnold

Pierre-Antoine Manzagol

Reza Babanezhad

Ioannis Mitliagkas

Nicolas Le Roux

Papers citing "Reducing the variance in online optimization by transporting past gradients"

6 / 6 papers shown

Title
ErrorCompensatedX: error compensation for variance reduced algorithms Hanlin Tang Yao Li Ji Liu Ming Yan 24 9 0 04 Aug 2021
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen Dami Choi Lukas Balles David Duvenaud Philipp Hennig ODL 36 6 0 09 Nov 2020
Demon: Improved Neural Network Training with Momentum Decay John Chen Cameron R. Wolfe Zhaoqi Li Anastasios Kyrillidis ODL 24 15 0 11 Oct 2019
Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods Nicolas Loizou Peter Richtárik 17 199 0 27 Dec 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 332 11,684 0 09 Mar 2017
A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method Simon Lacoste-Julien Mark W. Schmidt Francis R. Bach 126 259 0 10 Dec 2012