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Minimizing Finite Sums with the Stochastic Average Gradient

Minimizing Finite Sums with the Stochastic Average Gradient

10 September 2013
Mark Schmidt
Nicolas Le Roux
Francis R. Bach
ArXivPDFHTML

Papers citing "Minimizing Finite Sums with the Stochastic Average Gradient"

50 / 504 papers shown
Title
Distributed Optimization with Arbitrary Local Solvers
Distributed Optimization with Arbitrary Local Solvers
Chenxin Ma
Jakub Konecný
Martin Jaggi
Virginia Smith
Michael I. Jordan
Peter Richtárik
Martin Takáč
27
197
0
13 Dec 2015
Active Sampler: Light-weight Accelerator for Complex Data Analytics at
  Scale
Active Sampler: Light-weight Accelerator for Complex Data Analytics at Scale
Jinyang Gao
H. V. Jagadish
Beng Chin Ooi
14
18
0
12 Dec 2015
Efficient Distributed SGD with Variance Reduction
Efficient Distributed SGD with Variance Reduction
Soham De
Tom Goldstein
9
39
0
09 Dec 2015
Variance Reduction for Distributed Stochastic Gradient Descent
Variance Reduction for Distributed Stochastic Gradient Descent
Soham De
Gavin Taylor
Tom Goldstein
12
8
0
05 Dec 2015
Newton-Stein Method: An optimization method for GLMs via Stein's Lemma
Newton-Stein Method: An optimization method for GLMs via Stein's Lemma
Murat A. Erdogdu
29
13
0
28 Nov 2015
Fast clustering for scalable statistical analysis on structured images
Fast clustering for scalable statistical analysis on structured images
B. Thirion
Andrés Hoyos-Idrobo
J. Kahn
Gaël Varoquaux
18
0
0
16 Nov 2015
Speed learning on the fly
Speed learning on the fly
Pierre-Yves Massé
Yann Ollivier
23
13
0
08 Nov 2015
Dual Free Adaptive Mini-batch SDCA for Empirical Risk Minimization
Dual Free Adaptive Mini-batch SDCA for Empirical Risk Minimization
Xi He
Martin Takávc
29
1
0
22 Oct 2015
SGD with Variance Reduction beyond Empirical Risk Minimization
SGD with Variance Reduction beyond Empirical Risk Minimization
M. Achab
Agathe Guilloux
Stéphane Gaïffas
Emmanuel Bacry
27
5
0
16 Oct 2015
New Optimisation Methods for Machine Learning
New Optimisation Methods for Machine Learning
Aaron Defazio
46
6
0
09 Oct 2015
Stochastic gradient descent methods for estimation with large data sets
Stochastic gradient descent methods for estimation with large data sets
Dustin Tran
Panos Toulis
E. Airoldi
14
14
0
22 Sep 2015
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization
S. Sra
Adams Wei Yu
Mu Li
Alex Smola
ODL
17
30
0
20 Aug 2015
Doubly Stochastic Primal-Dual Coordinate Method for Bilinear
  Saddle-Point Problem
Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem
Adams Wei Yu
Qihang Lin
Tianbao Yang
30
7
0
14 Aug 2015
Convergence rates of sub-sampled Newton methods
Convergence rates of sub-sampled Newton methods
Murat A. Erdogdu
Andrea Montanari
40
156
0
12 Aug 2015
Distributed Mini-Batch SDCA
Distributed Mini-Batch SDCA
Martin Takáč
Peter Richtárik
Nathan Srebro
27
50
0
29 Jul 2015
A Bayesian Approach for Online Classifier Ensemble
A Bayesian Approach for Online Classifier Ensemble
Qinxun Bai
Henry Lam
Stan Sclaroff
BDL
35
0
0
08 Jul 2015
An optimal randomized incremental gradient method
An optimal randomized incremental gradient method
Guanghui Lan
Yi Zhou
34
220
0
08 Jul 2015
Stochastic Gradient Made Stable: A Manifold Propagation Approach for
  Large-Scale Optimization
Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization
Yadong Mu
Wei Liu
Wei Fan
31
32
0
28 Jun 2015
Splash: User-friendly Programming Interface for Parallelizing Stochastic
  Algorithms
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
Yuchen Zhang
Michael I. Jordan
31
20
0
24 Jun 2015
On Variance Reduction in Stochastic Gradient Descent and its
  Asynchronous Variants
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
Sashank J. Reddi
Ahmed S. Hefny
S. Sra
Barnabás Póczós
Alex Smola
38
194
0
23 Jun 2015
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle
  Point Problems
Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems
Zhanxing Zhu
Amos J. Storkey
ODL
27
19
0
12 Jun 2015
Variance Reduced Stochastic Gradient Descent with Neighbors
Variance Reduced Stochastic Gradient Descent with Neighbors
Thomas Hofmann
Aurelien Lucchi
Simon Lacoste-Julien
Brian McWilliams
ODL
31
153
0
11 Jun 2015
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums
Accelerated Stochastic Gradient Descent for Minimizing Finite Sums
Atsushi Nitanda
ODL
35
24
0
09 Jun 2015
Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex
  Losses
Primal Method for ERM with Flexible Mini-batching Schemes and Non-convex Losses
Dominik Csiba
Peter Richtárik
33
23
0
07 Jun 2015
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Zeyuan Allen-Zhu
Yang Yuan
29
195
0
05 Jun 2015
Efficient Elastic Net Regularization for Sparse Linear Models
Efficient Elastic Net Regularization for Sparse Linear Models
Zachary Chase Lipton
Charles Elkan
11
4
0
24 May 2015
Mind the duality gap: safer rules for the Lasso
Mind the duality gap: safer rules for the Lasso
Olivier Fercoq
Alexandre Gramfort
Joseph Salmon
44
138
0
13 May 2015
Towards stability and optimality in stochastic gradient descent
Towards stability and optimality in stochastic gradient descent
Panos Toulis
Dustin Tran
E. Airoldi
26
56
0
10 May 2015
Non-Uniform Stochastic Average Gradient Method for Training Conditional
  Random Fields
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields
Mark Schmidt
Reza Babanezhad
Mohamed Osama Ahmed
Aaron Defazio
Ann Clifton
Anoop Sarkar
35
83
0
16 Apr 2015
Expectation Propagation in the large-data limit
Expectation Propagation in the large-data limit
Guillaume P. Dehaene
Simon Barthelmé
24
44
0
27 Mar 2015
Stochastic Dual Coordinate Ascent with Adaptive Probabilities
Stochastic Dual Coordinate Ascent with Adaptive Probabilities
Dominik Csiba
Zheng Qu
Peter Richtárik
ODL
61
97
0
27 Feb 2015
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
Zheng Qu
Peter Richtárik
Martin Takáč
Olivier Fercoq
ODL
40
98
0
08 Feb 2015
Microscopic Advances with Large-Scale Learning: Stochastic Optimization
  for Cryo-EM
Microscopic Advances with Large-Scale Learning: Stochastic Optimization for Cryo-EM
A. Punjani
Marcus A. Brubaker
29
3
0
19 Jan 2015
Communication-Efficient Distributed Optimization of Self-Concordant
  Empirical Loss
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
Yuchen Zhang
Lin Xiao
38
72
0
01 Jan 2015
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity
Zheng Qu
Peter Richtárik
24
131
0
27 Dec 2014
Constant Step Size Least-Mean-Square: Bias-Variance Trade-offs and
  Optimal Sampling Distributions
Constant Step Size Least-Mean-Square: Bias-Variance Trade-offs and Optimal Sampling Distributions
Alexandre Défossez
Francis R. Bach
31
13
0
29 Nov 2014
Randomized Dual Coordinate Ascent with Arbitrary Sampling
Randomized Dual Coordinate Ascent with Arbitrary Sampling
Zheng Qu
Peter Richtárik
Tong Zhang
38
58
0
21 Nov 2014
SelfieBoost: A Boosting Algorithm for Deep Learning
SelfieBoost: A Boosting Algorithm for Deep Learning
Shai Shalev-Shwartz
FedML
35
17
0
13 Nov 2014
A Lower Bound for the Optimization of Finite Sums
A Lower Bound for the Optimization of Finite Sums
Alekh Agarwal
Léon Bottou
33
124
0
02 Oct 2014
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk
  Minimization
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
Yuchen Zhang
Xiao Lin
43
261
0
10 Sep 2014
Global Convergence of Online Limited Memory BFGS
Global Convergence of Online Limited Memory BFGS
Aryan Mokhtari
Alejandro Ribeiro
30
164
0
06 Sep 2014
On Data Preconditioning for Regularized Loss Minimization
On Data Preconditioning for Regularized Loss Minimization
Tianbao Yang
Rong Jin
Shenghuo Zhu
Qihang Lin
48
9
0
13 Aug 2014
Finito: A Faster, Permutable Incremental Gradient Method for Big Data
  Problems
Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems
Aaron Defazio
T. Caetano
Justin Domke
57
169
0
10 Jul 2014
A Coordinate Descent Primal-Dual Algorithm and Application to
  Distributed Asynchronous Optimization
A Coordinate Descent Primal-Dual Algorithm and Application to Distributed Asynchronous Optimization
Pascal Bianchi
W. Hachem
F. Iutzeler
62
57
0
03 Jul 2014
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly
  Convex Composite Objectives
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Aaron Defazio
Francis R. Bach
Simon Lacoste-Julien
ODL
44
1,814
0
01 Jul 2014
Randomized Block Coordinate Descent for Online and Stochastic
  Optimization
Randomized Block Coordinate Descent for Online and Stochastic Optimization
Huahua Wang
A. Banerjee
ODL
55
36
0
01 Jul 2014
Smoothed Gradients for Stochastic Variational Inference
Smoothed Gradients for Stochastic Variational Inference
Stephan Mandt
David M. Blei
BDL
DiffM
44
29
0
13 Jun 2014
Peacock: Learning Long-Tail Topic Features for Industrial Applications
Peacock: Learning Long-Tail Topic Features for Industrial Applications
Yi Wang
Xuemin Zhao
Zhenlong Sun
Hao Yan
Lifeng Wang
Zhihui Jin
Liubin Wang
Yang Gao
Ching Law
Jia Zeng
AI4TS
109
61
0
17 May 2014
A Proximal Stochastic Gradient Method with Progressive Variance
  Reduction
A Proximal Stochastic Gradient Method with Progressive Variance Reduction
Lin Xiao
Tong Zhang
ODL
93
737
0
19 Mar 2014
Incremental Majorization-Minimization Optimization with Application to
  Large-Scale Machine Learning
Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning
Julien Mairal
79
317
0
18 Feb 2014
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