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Minimizing Finite Sums with the Stochastic Average Gradient
v1v2 (latest)

Minimizing Finite Sums with the Stochastic Average Gradient

10 September 2013
Mark Schmidt
Nicolas Le Roux
Francis R. Bach
ArXiv (abs)PDFHTML

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

50 / 506 papers shown
Title
Glocal Smoothness: Line Search can really help!
Glocal Smoothness: Line Search can really help!
Curtis Fox
Aaron Mishkin
Sharan Vaswani
Mark Schmidt
39
2
0
14 Jun 2025
Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
Duc Toan Nguyen
Trang H. Tran
Lam M. Nguyen
15
0
0
14 Jun 2025
Computational Efficiency under Covariate Shift in Kernel Ridge Regression
Computational Efficiency under Covariate Shift in Kernel Ridge Regression
Andrea Della Vecchia
Arnaud Mavakala Watusadisi
Ernesto De Vito
Lorenzo Rosasco
47
0
0
20 May 2025
A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities
A Flexible Fairness Framework with Surrogate Loss Reweighting for Addressing Sociodemographic Disparities
Wen Xu
Elham Dolatabadi
FaML
116
0
0
21 Mar 2025
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
Xue Feng
M. Paul Laiu
Thomas Strohmer
FedML
84
0
0
14 Mar 2025
Effectively Leveraging Momentum Terms in Stochastic Line Search
  Frameworks for Fast Optimization of Finite-Sum Problems
Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
Matteo Lapucci
Davide Pucci
ODL
55
0
0
11 Nov 2024
Analysis of ELSA COVID-19 Substudy response rate using machine learning
  algorithms
Analysis of ELSA COVID-19 Substudy response rate using machine learning algorithms
Marjan Qazvini
114
0
0
01 Nov 2024
Sample-Efficient Agnostic Boosting
Sample-Efficient Agnostic Boosting
Udaya Ghai
Karan Singh
47
0
0
31 Oct 2024
Boosting the Performance of Decentralized Federated Learning via
  Catalyst Acceleration
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration
Qinglun Li
Miao Zhang
Yingqi Liu
Quanjun Yin
Li Shen
Xiaochun Cao
FedML
94
0
0
09 Oct 2024
OledFL: Unleashing the Potential of Decentralized Federated Learning via
  Opposite Lookahead Enhancement
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
Qinglun Li
Miao Zhang
Mengzhu Wang
Quanjun Yin
Li Shen
OODDFedML
64
0
0
09 Oct 2024
FedScalar: A Communication efficient Federated Learning
FedScalar: A Communication efficient Federated Learning
M. Rostami
S. S. Kia
FedML
91
2
0
03 Oct 2024
On the SAGA algorithm with decreasing step
On the SAGA algorithm with decreasing step
Luis Fredes
Bernard Bercu
Eméric Gbaguidi
51
1
0
02 Oct 2024
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
Matteo Lapucci
Davide Pucci
92
1
0
06 Aug 2024
Sequential Gaussian Variational Inference for Nonlinear State Estimation
  applied to Robotic Applications
Sequential Gaussian Variational Inference for Nonlinear State Estimation applied to Robotic Applications
Min-Won Seo
Solmaz S. Kia
72
0
0
07 Jul 2024
CUPID: Improving Battle Fairness and Position Satisfaction in Online
  MOBA Games with a Re-matchmaking System
CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System
Ge Fan
Chaoyun Zhang
Kai Wang
Yingjie Li
Junyang Chen
Zenglin Xu
81
1
0
28 Jun 2024
Stochastic Optimisation Framework using the Core Imaging Library and
  Synergistic Image Reconstruction Framework for PET Reconstruction
Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction
E. Papoutsellis
Casper O. da Costa-Luis
D. Deidda
C. Delplancke
Margaret Duff
...
Ž. Kereta
E. Ovtchinnikov
Edoardo Pasca
Georg Schramm
Kris Thielemans
116
1
0
21 Jun 2024
Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data
Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data
Xiaolu Wang
Yuchang Sun
Hoi-To Wai
Jun Zhang
75
0
0
27 May 2024
FedStale: leveraging stale client updates in federated learning
FedStale: leveraging stale client updates in federated learning
Angelo Rodio
Giovanni Neglia
FedML
70
5
0
07 May 2024
Second-order Information Promotes Mini-Batch Robustness in
  Variance-Reduced Gradients
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients
Sachin Garg
A. Berahas
Michal Dereziñski
87
1
0
23 Apr 2024
Stochastic Online Optimization for Cyber-Physical and Robotic Systems
Stochastic Online Optimization for Cyber-Physical and Robotic Systems
Hao Ma
Melanie Zeilinger
Michael Muehlebach
87
1
0
08 Apr 2024
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization
  with Loopless Variance Reduction
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction
Yury Demidovich
Grigory Malinovsky
Peter Richtárik
88
3
0
11 Mar 2024
Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity
Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity
Boao Kong
Shuchen Zhu
Songtao Lu
Xinmeng Huang
Kun Yuan
24
0
0
05 Feb 2024
On the Complexity of Finite-Sum Smooth Optimization under the
  Polyak-Łojasiewicz Condition
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition
Yunyan Bai
Yuxing Liu
Luo Luo
62
0
0
04 Feb 2024
Incremental Quasi-Newton Methods with Faster Superlinear Convergence
  Rates
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates
Zhuanghua Liu
Luo Luo
K. H. Low
102
2
0
04 Feb 2024
Continuous-time Riemannian SGD and SVRG Flows on Wasserstein
  Probabilistic Space
Continuous-time Riemannian SGD and SVRG Flows on Wasserstein Probabilistic Space
Mingyang Yi
Bohan Wang
65
0
0
24 Jan 2024
Accelerating Distributed Stochastic Optimization via Self-Repellent
  Random Walks
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
Jie Hu
Vishwaraj Doshi
Do Young Eun
96
3
0
18 Jan 2024
Stochastic optimization with arbitrary recurrent data sampling
Stochastic optimization with arbitrary recurrent data sampling
William G. Powell
Hanbaek Lyu
78
0
0
15 Jan 2024
Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
Antoine Godichon-Baggioni
Wei Lu
Bruno Portier
165
1
0
15 Jan 2024
Improving the Privacy and Practicality of Objective Perturbation for
  Differentially Private Linear Learners
Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
Rachel Redberg
Antti Koskela
Yu-Xiang Wang
136
6
0
31 Dec 2023
A Coefficient Makes SVRG Effective
A Coefficient Makes SVRG Effective
Yida Yin
Zhiqiu Xu
Zhiyuan Li
Trevor Darrell
Zhuang Liu
87
1
0
09 Nov 2023
Computing Approximate $\ell_p$ Sensitivities
Computing Approximate ℓp\ell_pℓp​ Sensitivities
Swati Padmanabhan
David P. Woodruff
Qiuyi Zhang
82
0
0
07 Nov 2023
Signal Processing Meets SGD: From Momentum to Filter
Signal Processing Meets SGD: From Momentum to Filter
Zhipeng Yao
Guisong Chang
Jiaqi Zhang
Qi Zhang
Dazhou Li
Yu Zhang
ODL
105
0
0
06 Nov 2023
A Variational Perspective on High-Resolution ODEs
A Variational Perspective on High-Resolution ODEs
Hoomaan Maskan
K. C. Zygalakis
A. Yurtsever
80
3
0
03 Nov 2023
Variance-Reduced Stochastic Optimization for Efficient Inference of
  Hidden Markov Models
Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov Models
E. Sidrow
Nancy Heckman
Alexandre Bouchard-Coté
S. Fortune
A. Trites
M. Auger-Méthé
96
1
0
06 Oct 2023
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions
Xu Cai
Ahmet Alacaoglu
Jelena Diakonikolas
99
8
0
04 Oct 2023
On the Parallel Complexity of Multilevel Monte Carlo in Stochastic
  Gradient Descent
On the Parallel Complexity of Multilevel Monte Carlo in Stochastic Gradient Descent
Kei Ishikawa
BDL
82
0
0
03 Oct 2023
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming
  Data
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
Xiaolu Wang
Cheng Jin
Hoi-To Wai
Yuantao Gu
61
4
0
10 Sep 2023
Variance reduction techniques for stochastic proximal point algorithms
Variance reduction techniques for stochastic proximal point algorithms
Cheik Traoré
Vassilis Apidopoulos
Saverio Salzo
S. Villa
64
5
0
18 Aug 2023
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence
  and Variance Reduction
Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction
Xiao-Yan Jiang
Sebastian U. Stich
80
20
0
11 Aug 2023
Faster Stochastic Algorithms for Minimax Optimization under
  Polyak--Łojasiewicz Conditions
Faster Stochastic Algorithms for Minimax Optimization under Polyak--Łojasiewicz Conditions
Le‐Yu Chen
Boyuan Yao
Luo Luo
64
15
0
29 Jul 2023
Linear Convergence of Black-Box Variational Inference: Should We Stick
  the Landing?
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
Kyurae Kim
Yian Ma
Jacob R. Gardner
103
7
0
27 Jul 2023
A hybrid machine learning framework for clad characteristics prediction
  in metal additive manufacturing
A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing
S. Tayebati
K. Cho
AI4CE
44
3
0
04 Jul 2023
Efficient preconditioned stochastic gradient descent for estimation in
  latent variable models
Efficient preconditioned stochastic gradient descent for estimation in latent variable models
C. Baey
Maud Delattre
E. Kuhn
Jean-Benoist Léger
Sarah Lemler
40
4
0
22 Jun 2023
Sharpened Lazy Incremental Quasi-Newton Method
Sharpened Lazy Incremental Quasi-Newton Method
Aakash Lahoti
Spandan Senapati
K. Rajawat
Alec Koppel
108
2
0
26 May 2023
SignSVRG: fixing SignSGD via variance reduction
SignSVRG: fixing SignSGD via variance reduction
Evgenii Chzhen
S. Schechtman
117
3
0
22 May 2023
Online Learning Under A Separable Stochastic Approximation Framework
Online Learning Under A Separable Stochastic Approximation Framework
Min Gan
Xiang-Xiang Su
Guang-yong Chen
Jing Chen
49
0
0
12 May 2023
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates
  and Practical Features
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Aleksandr Beznosikov
David Dobre
Gauthier Gidel
76
6
0
23 Apr 2023
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale
  Empirical Risk Minimization
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Zebang Shen
Hui Qian
Tongzhou Mu
Chao Zhang
ODL
44
0
0
23 Apr 2023
Debiasing Conditional Stochastic Optimization
Debiasing Conditional Stochastic Optimization
Lie He
S. Kasiviswanathan
CMLBDL
114
4
0
20 Apr 2023
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms
  for Optimization under Orthogonality Constraints
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
Pierre Ablin
Simon Vary
Bin Gao
P.-A. Absil
59
10
0
29 Mar 2023
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