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1407.0202
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SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Neural Information Processing Systems (NeurIPS), 2014
1 July 2014
Aaron Defazio
Francis R. Bach
Damien Scieur
ODL
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Papers citing
"SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives"
50 / 878 papers shown
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
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23 Mar 2017
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Hiroyuki Sato
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15 Mar 2017
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
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Lin Xiao
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07 Mar 2017
Learn-and-Adapt Stochastic Dual Gradients for Network Resource Allocation
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Qing Ling
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Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
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Taiji Suzuki
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01 Mar 2017
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Jie Liu
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392
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01 Mar 2017
Optimal algorithms for smooth and strongly convex distributed optimization in networks
International Conference on Machine Learning (ICML), 2017
Kevin Scaman
Francis R. Bach
Sébastien Bubeck
Y. Lee
Laurent Massoulié
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348
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28 Feb 2017
Stochastic Variance Reduction Methods for Policy Evaluation
International Conference on Machine Learning (ICML), 2017
S. Du
Jianshu Chen
Lihong Li
Lin Xiao
Dengyong Zhou
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208
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25 Feb 2017
Stochastic Composite Least-Squares Regression with convergence rate O(1/n)
Annual Conference Computational Learning Theory (COLT), 2017
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Francis R. Bach
167
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21 Feb 2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox
Annual Conference Computational Learning Theory (COLT), 2017
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Weiran Wang
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0
21 Feb 2017
SAGA and Restricted Strong Convexity
Chao Qu
Yan Li
Huan Xu
149
5
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19 Feb 2017
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport
SIAM Journal on Optimization (SIAM J. Optim.), 2016
Hiroyuki Sato
Hiroyuki Kasai
Bamdev Mishra
327
59
0
18 Feb 2017
Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter
International Conference on Machine Learning (ICML), 2017
Zeyuan Allen-Zhu
466
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02 Feb 2017
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate
SIAM Journal on Optimization (SIAM J. Optim.), 2017
Aryan Mokhtari
Mark Eisen
Alejandro Ribeiro
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02 Feb 2017
Linear convergence of SDCA in statistical estimation
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Huan Xu
147
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26 Jan 2017
An Asynchronous Parallel Approach to Sparse Recovery
Information Theory and Applications Workshop (ITA), 2017
Deanna Needell
T. Woolf
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12 Jan 2017
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery
Lingxiao Wang
Xiao Zhang
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09 Jan 2017
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements
Xiao Zhang
Lingxiao Wang
Quanquan Gu
224
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02 Jan 2017
Asymptotic Optimality in Stochastic Optimization
Annals of Statistics (Ann. Stat.), 2016
John C. Duchi
Feng Ruan
198
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16 Dec 2016
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption
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Martin Takáč
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259
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16 Dec 2016
Coupling Adaptive Batch Sizes with Learning Rates
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Javier Romero
Philipp Hennig
ODL
241
122
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15 Dec 2016
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space
Alec Koppel
Garrett A. Warnell
Ethan Stump
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13 Dec 2016
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems
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Jean Lafond
Anna Scaglione
Eric Moulines
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Subsampled online matrix factorization with convergence guarantees
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Julien Mairal
Gaël Varoquaux
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30 Nov 2016
Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher
Brian McWilliams
Yannic Kilcher
J. M. Buhmann
N. Meinshausen
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106
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21 Nov 2016
Accelerated Variance Reduced Block Coordinate Descent
Zebang Shen
Hui Qian
Chao Zhang
Tengfei Zhou
96
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13 Nov 2016
Greedy Step Averaging: A parameter-free stochastic optimization method
Xiatian Zhang
Fan Yao
Yongjun Tian
112
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11 Nov 2016
Linear Convergence of SVRG in Statistical Estimation
Chao Qu
Yan Li
Huan Xu
214
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07 Nov 2016
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate
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Mert Gurbuzbalaban
Alejandro Ribeiro
318
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01 Nov 2016
Big Batch SGD: Automated Inference using Adaptive Batch Sizes
Soham De
A. Yadav
David Jacobs
Tom Goldstein
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445
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18 Oct 2016
Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server
Arda Aytekin
Hamid Reza Feyzmahdavian
M. Johansson
207
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18 Oct 2016
Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification
Prateek Jain
Sham Kakade
Rahul Kidambi
Praneeth Netrapalli
Aaron Sidford
MoMe
369
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12 Oct 2016
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
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John C. Duchi
Peter Glynn
Hongseok Namkoong
450
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11 Oct 2016
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
International Conference on Artificial Intelligence and Statistics (AISTATS), 2016
Jialei Wang
Jason D. Lee
M. Mahdavi
Mladen Kolar
Nathan Srebro
226
51
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10 Oct 2016
Stochastic Alternating Direction Method of Multipliers with Variance Reduction for Nonconvex Optimization
Feihu Huang
Songcan Chen
Zhaosong Lu
414
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10 Oct 2016
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konecný
H. B. McMahan
Daniel Ramage
Peter Richtárik
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444
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08 Oct 2016
Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation
IEEE Transactions on Signal Processing (IEEE TSP), 2016
Tianyi Chen
Aryan Mokhtari
Xin Wang
Alejandro Ribeiro
G. Giannakis
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07 Oct 2016
A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning
Aleksandr Aravkin
Damek Davis
352
21
0
04 Oct 2016
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure
Neural Information Processing Systems (NeurIPS), 2016
A. Bietti
Julien Mairal
506
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04 Oct 2016
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration
SIAM Journal on Optimization (SIAM J. Optim.), 2016
Hongzhou Lin
Julien Mairal
Zaïd Harchaoui
271
13
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A Primer on Coordinate Descent Algorithms
Hao-Jun Michael Shi
Shenyinying Tu
Yangyang Xu
W. Yin
308
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Decoupled Asynchronous Proximal Stochastic Gradient Descent with Variance Reduction
Zhouyuan Huo
Bin Gu
Heng-Chiao Huang
122
4
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22 Sep 2016
Gray-box inference for structured Gaussian process models
P. Galliani
Amir Dezfouli
Edwin V. Bonilla
Novi Quadrianto
BDL
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0
14 Sep 2016
Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method
Lihua Lei
Sai Li
236
100
0
12 Sep 2016
AIDE: Fast and Communication Efficient Distributed Optimization
Sashank J. Reddi
Jakub Konecný
Peter Richtárik
Barnabás Póczós
Alex Smola
178
152
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24 Aug 2016
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates
Tianbao Yang
Qihang Lin
Lijun Zhang
229
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Stochastic Frank-Wolfe Methods for Nonconvex Optimization
Sashank J. Reddi
S. Sra
Barnabás Póczós
Alex Smola
208
150
0
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Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization
SIAM Journal on Optimization (SIAM J. Optim.), 2016
Tianlin Li
Shiqian Ma
Shiqian Ma
Wen Liu
328
187
0
05 Jul 2016
Accelerate Stochastic Subgradient Method by Leveraging Local Growth Condition
Analysis and Applications (AA), 2016
Yi Tian Xu
Qihang Lin
Tianbao Yang
416
11
0
04 Jul 2016
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