ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1506.03662
  4. Cited By
Variance Reduced Stochastic Gradient Descent with Neighbors

Variance Reduced Stochastic Gradient Descent with Neighbors

11 June 2015
Thomas Hofmann
Aurelien Lucchi
Simon Lacoste-Julien
Brian McWilliams
    ODL
ArXivPDFHTML

Papers citing "Variance Reduced Stochastic Gradient Descent with Neighbors"

30 / 30 papers shown
Title
SOREL: A Stochastic Algorithm for Spectral Risks Minimization
SOREL: A Stochastic Algorithm for Spectral Risks Minimization
Yuze Ge
Rujun Jiang
38
0
0
19 Jul 2024
AdaSelection: Accelerating Deep Learning Training through Data
  Subsampling
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
Minghe Zhang
Chaosheng Dong
Jinmiao Fu
Tianchen Zhou
Jia Liang
...
Bo Liu
Michinari Momma
Bryan Wang
Yan Gao
Yi Sun
35
3
0
19 Jun 2023
Stochastic Optimization for Spectral Risk Measures
Stochastic Optimization for Spectral Risk Measures
Ronak R. Mehta
Vincent Roulet
Krishna Pillutla
Lang Liu
Zaïd Harchaoui
32
6
0
10 Dec 2022
Statistical and Computational Guarantees for Influence Diagnostics
Statistical and Computational Guarantees for Influence Diagnostics
Jillian R. Fisher
Lang Liu
Krishna Pillutla
Y. Choi
Zaïd Harchaoui
TDI
26
0
0
08 Dec 2022
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with
  Application to Distributed Optimization
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization
Luyao Guo
Jinde Cao
Xinli Shi
Shaofu Yang
15
0
0
06 Dec 2022
Green, Quantized Federated Learning over Wireless Networks: An
  Energy-Efficient Design
Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design
Minsu Kim
Walid Saad
Mohammad Mozaffari
Merouane Debbah
FedML
MQ
23
27
0
19 Jul 2022
Stochastic Gradient Methods with Preconditioned Updates
Stochastic Gradient Methods with Preconditioned Updates
Abdurakhmon Sadiev
Aleksandr Beznosikov
Abdulla Jasem Almansoori
Dmitry Kamzolov
R. Tappenden
Martin Takáč
ODL
34
9
0
01 Jun 2022
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient
  Methods
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Aleksandr Beznosikov
Eduard A. Gorbunov
Hugo Berard
Nicolas Loizou
19
49
0
15 Feb 2022
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural
  Networks
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks
R. Waleffe
J. Mohoney
Theodoros Rekatsinas
Shivaram Venkataraman
GNN
29
25
0
04 Feb 2022
Decentralized Stochastic Variance Reduced Extragradient Method
Decentralized Stochastic Variance Reduced Extragradient Method
Luo Luo
Haishan Ye
27
7
0
01 Feb 2022
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free
  Optimization
Accelerating Perturbed Stochastic Iterates in Asynchronous Lock-Free Optimization
Kaiwen Zhou
Anthony Man-Cho So
James Cheng
19
1
0
30 Sep 2021
Stochastic Polyak Stepsize with a Moving Target
Stochastic Polyak Stepsize with a Moving Target
Robert Mansel Gower
Aaron Defazio
Michael G. Rabbat
32
17
0
22 Jun 2021
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
Jianhong Wang
Yuan Zhang
Yunjie Gu
Tae-Kyun Kim
OffRL
FAtt
22
19
0
31 May 2021
SVRG Meets AdaGrad: Painless Variance Reduction
SVRG Meets AdaGrad: Painless Variance Reduction
Benjamin Dubois-Taine
Sharan Vaswani
Reza Babanezhad
Mark W. Schmidt
Simon Lacoste-Julien
18
18
0
18 Feb 2021
Local SGD: Unified Theory and New Efficient Methods
Local SGD: Unified Theory and New Efficient Methods
Eduard A. Gorbunov
Filip Hanzely
Peter Richtárik
FedML
37
109
0
03 Nov 2020
Optimization for Supervised Machine Learning: Randomized Algorithms for
  Data and Parameters
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters
Filip Hanzely
34
0
0
26 Aug 2020
Stochastic Hamiltonian Gradient Methods for Smooth Games
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou
Hugo Berard
Alexia Jolicoeur-Martineau
Pascal Vincent
Simon Lacoste-Julien
Ioannis Mitliagkas
39
50
0
08 Jul 2020
Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic
  Gradient Methods
Sampling and Update Frequencies in Proximal Variance-Reduced Stochastic Gradient Methods
Martin Morin
Pontus Giselsson
27
4
0
13 Feb 2020
Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization
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
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems
Filip Hanzely
D. Kovalev
Peter Richtárik
35
17
0
11 Feb 2020
Reducing the variance in online optimization by transporting past
  gradients
Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold
Pierre-Antoine Manzagol
Reza Babanezhad
Ioannis Mitliagkas
Nicolas Le Roux
24
28
0
08 Jun 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient
Reducing Noise in GAN Training with Variance Reduced Extragradient
Tatjana Chavdarova
Gauthier Gidel
F. Fleuret
Simon Lacoste-Julien
25
134
0
18 Apr 2019
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic
  Gradient Methods
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods
Martin Morin
Pontus Giselsson
20
2
0
21 Mar 2019
Improved asynchronous parallel optimization analysis for stochastic
  incremental methods
Improved asynchronous parallel optimization analysis for stochastic incremental methods
Rémi Leblond
Fabian Pedregosa
Simon Lacoste-Julien
16
70
0
11 Jan 2018
Sub-sampled Cubic Regularization for Non-convex Optimization
Sub-sampled Cubic Regularization for Non-convex Optimization
Jonas Köhler
Aurelien Lucchi
19
164
0
16 May 2017
Stochastic Optimization with Variance Reduction for Infinite Datasets
  with Finite-Sum Structure
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure
A. Bietti
Julien Mairal
44
36
0
04 Oct 2016
Less than a Single Pass: Stochastically Controlled Stochastic Gradient
  Method
Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method
Lihua Lei
Michael I. Jordan
29
96
0
12 Sep 2016
ASAGA: Asynchronous Parallel SAGA
ASAGA: Asynchronous Parallel SAGA
Rémi Leblond
Fabian Pedregosa
Simon Lacoste-Julien
AI4TS
29
101
0
15 Jun 2016
Variance-Reduced Proximal Stochastic Gradient Descent for Non-convex Composite optimization
Xiyu Yu
Dacheng Tao
21
5
0
02 Jun 2016
A Simple Practical Accelerated Method for Finite Sums
A Simple Practical Accelerated Method for Finite Sums
Aaron Defazio
28
121
0
08 Feb 2016
1