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1407.0202
Cited By

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly
Convex Composite Objectives

v1v2v3 (latest)

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

Neural Information Processing Systems (NeurIPS), 2014

1 July 2014

Francis R. Bach

ArXiv (abs)PDF HTML

Papers citing "SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives"

50 / 878 papers shown

A Dual Accelerated Method for Online Stochastic Distributed Averaging:
From Consensus to Decentralized Policy Evaluation

A Dual Accelerated Method for Online Stochastic Distributed Averaging: From Consensus to Decentralized Policy EvaluationIEEE Transactions on Automatic Control (TAC), 2022

266

4

0

23 Jul 2022

Riemannian Stochastic Gradient Method for Nested Composition
Optimization

Riemannian Stochastic Gradient Method for Nested Composition OptimizationIEEE Conference on Decision and Control (CDC), 2022

236

1

0

19 Jul 2022

Multi-block-Single-probe Variance Reduced Estimator for Coupled
Compositional Optimization

Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional OptimizationNeural Information Processing Systems (NeurIPS), 2022

308

18

0

18 Jul 2022

SPIRAL: A superlinearly convergent incremental proximal algorithm for
nonconvex finite sum minimization

SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimizationComputational optimization and applications (Comput. Optim. Appl.), 2022

Pourya Behmandpoor

Andreas Themelis

Panagiotis Patrinos

219

2

0

17 Jul 2022

Adaptive Sketches for Robust Regression with Importance Sampling

Adaptive Sketches for Robust Regression with Importance SamplingInternational Workshop and International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), 2022

David P. Woodruff

151

6

0

16 Jul 2022

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent
Kernels

TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent KernelsNeural Information Processing Systems (NeurIPS), 2022

Sai Praneeth Karimireddy

Michael I. Jordan

257

33

0

13 Jul 2022

Variance Reduced ProxSkip: Algorithm, Theory and Application to
Federated Learning

Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated LearningNeural Information Processing Systems (NeurIPS), 2022

Grigory Malinovsky

Peter Richtárik

290

42

0

09 Jul 2022

Tackling Data Heterogeneity: A New Unified Framework for Decentralized
SGD with Sample-induced Topology

Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced TopologyInternational Conference on Machine Learning (ICML), 2022

184

18

0

08 Jul 2022

Benchopt: Reproducible, efficient and collaborative optimization
benchmarks

Benchopt: Reproducible, efficient and collaborative optimization benchmarksNeural Information Processing Systems (NeurIPS), 2022

Mathurin Massias

Alexandre Gramfort

Pierre Ablin

Pierre-Antoine Bannier Benjamin Charlier

...

Binh Duc Nguyen

A. Rakotomamonjy

Zaccharie Ramzi

289

48

0

27 Jun 2022

Finding Optimal Policy for Queueing Models: New Parameterization

Finding Optimal Policy for Queueing Models: New Parameterization

128

2

0

21 Jun 2022

SoteriaFL: A Unified Framework for Private Federated Learning with
Communication Compression

SoteriaFL: A Unified Framework for Private Federated Learning with Communication CompressionNeural Information Processing Systems (NeurIPS), 2022

244

49

0

20 Jun 2022

MF-OMO: An Optimization Formulation of Mean-Field Games

MF-OMO: An Optimization Formulation of Mean-Field GamesSIAM Journal of Control and Optimization (SICON), 2022

272

18

0

20 Jun 2022

Stability and Generalization of Stochastic Optimization with Nonconvex
and Nonsmooth Problems

Stability and Generalization of Stochastic Optimization with Nonconvex and Nonsmooth ProblemsAnnual Conference Computational Learning Theory (COLT), 2022

257

23

0

14 Jun 2022

Anchor Sampling for Federated Learning with Partial Client Participation

Anchor Sampling for Federated Learning with Partial Client ParticipationInternational Conference on Machine Learning (ICML), 2022

Jing Gao

228

24

0

13 Jun 2022

On the Convergence to a Global Solution of Shuffling-Type Gradient
Algorithms

On the Convergence to a Global Solution of Shuffling-Type Gradient AlgorithmsNeural Information Processing Systems (NeurIPS), 2022

247

3

0

13 Jun 2022

Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in
Federated Learning

Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated LearningInternational Conference on Internet-of-Things Design and Implementation (IoTDI), 2022

Edith C.H. Ngai

340

16

0

10 Jun 2022

Push--Pull with Device Sampling

Push--Pull with Device SamplingIEEE Transactions on Automatic Control (TAC), 2022

166

2

0

08 Jun 2022

Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches

Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches

Michal Derezinski

327

11

0

06 Jun 2022

Sharper Rates and Flexible Framework for Nonconvex SGD with Client and
Data Sampling

Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling

Alexander Tyurin

Konstantin Burlachenko

Peter Richtárik

136

9

0

05 Jun 2022

Federated Adversarial Training with Transformers

Federated Adversarial Training with Transformers

Ahmed Aldahdooh

Olivier Déforges

220

2

0

05 Jun 2022

A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of
Stability in Training Neural Networks

A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural NetworksJournal of machine learning research (JMLR), 2022

G. Sundaramoorthi

411

2

0

04 Jun 2022

From $t$-SNE to UMAP with contrastive learning

t

-SNE to UMAP with contrastive learningInternational Conference on Learning Representations (ICLR), 2022

Sebastian Damrich

Jan Niklas Böhm

345

29

0

03 Jun 2022

Walk for Learning: A Random Walk Approach for Federated Learning from
Heterogeneous Data

Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous DataIEEE Journal on Selected Areas in Communications (JSAC), 2022

140

30

0

01 Jun 2022

Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker
Assumptions and Communication Compression as a Cherry on the Top

Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top

Eduard A. Gorbunov

Samuel Horváth

Peter Richtárik

287

0

0

01 Jun 2022

Stochastic Gradient Methods with Preconditioned Updates

Stochastic Gradient Methods with Preconditioned UpdatesJournal of Optimization Theory and Applications (JOTA), 2022

Abdurakhmon Sadiev

Aleksandr Beznosikov

Abdulla Jasem Almansoori

Dmitry Kamzolov

269

12

0

01 Jun 2022

A principled framework for the design and analysis of token algorithms

A principled framework for the design and analysis of token algorithmsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Aymeric Dieuleveut

222

17

0

30 May 2022

Confederated Learning: Federated Learning with Decentralized Edge
Servers

Confederated Learning: Federated Learning with Decentralized Edge ServersIEEE Transactions on Signal Processing (IEEE Trans. Signal Process.), 2022

230

31

0

30 May 2022

Stochastic Gradient Methods with Compressed Communication for
Decentralized Saddle Point Problems

Stochastic Gradient Methods with Compressed Communication for Decentralized Saddle Point Problems

Vishnu Narayanan

159

2

0

28 May 2022

Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic
Decentralized Optimization

Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization

198

4

0

23 May 2022

SADAM: Stochastic Adam, A Stochastic Operator for First-Order
Gradient-based Optimizer

SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer

Wei Zhang

197

2

0

20 May 2022

On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning

On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning

Angeles Martinez

128

1

0

18 May 2022

Federated Random Reshuffling with Compression and Variance Reduction

Federated Random Reshuffling with Compression and Variance Reduction

Grigory Malinovsky

Peter Richtárik

300

12

0

08 May 2022

Communication Compression for Decentralized Learning with Operator
Splitting Methods

Communication Compression for Decentralized Learning with Operator Splitting MethodsIEEE Transactions on Signal and Information Processing over Networks (TSIPN), 2022

206

3

0

08 May 2022

Byzantine Fault Tolerance in Distributed Machine Learning : a Survey

Byzantine Fault Tolerance in Distributed Machine Learning : a Survey

Djamila Bouhata

Ahcène Bounceur

301

9

0

05 May 2022

An Adaptive Incremental Gradient Method With Support for Non-Euclidean
Norms

An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms

183

1

0

28 Apr 2022

Neighbor-Based Optimized Logistic Regression Machine Learning Model For
Electric Vehicle Occupancy Detection

Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection

51

1

0

28 Apr 2022

FedShuffle: Recipes for Better Use of Local Work in Federated Learning

FedShuffle: Recipes for Better Use of Local Work in Federated Learning

Samuel Horváth

Peter Richtárik

Michael G. Rabbat

283

22

0

27 Apr 2022

FedCau: A Proactive Stop Policy for Communication and Computation
Efficient Federated Learning

FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated LearningIEEE Transactions on Wireless Communications (TWC), 2022

Afsaneh Mahmoudi

H. S. Ghadikolaei

José Hélio da Cruz Júnior

Carlo Fischione

108

11

0

16 Apr 2022

A Semismooth Newton Stochastic Proximal Point Algorithm with Variance
Reduction

A Semismooth Newton Stochastic Proximal Point Algorithm with Variance ReductionSIAM Journal on Optimization (SIAM J. Optim.), 2022

228

8

0

01 Apr 2022

An Adaptive Gradient Method with Energy and Momentum

An Adaptive Gradient Method with Energy and MomentumAnnals of Applied Mathematics (AAM), 2022

158

10

0

23 Mar 2022

Closing the Generalization Gap of Cross-silo Federated Medical Image
Segmentation

Closing the Generalization Gap of Cross-silo Federated Medical Image SegmentationComputer Vision and Pattern Recognition (CVPR), 2022

Wenqi Li

Ali Hatamizadeh

Heng-Chiao Huang

Ziyue Xu

193

66

0

18 Mar 2022

Learning Distributionally Robust Models at Scale via Composite
Optimization

Learning Distributionally Robust Models at Scale via Composite OptimizationInternational Conference on Learning Representations (ICLR), 2022

Farzin Haddadpour

Mohammad Mahdi Kamani

165

5

0

17 Mar 2022

Stochastic Halpern Iteration with Variance Reduction for Stochastic
Monotone Inclusions

Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone InclusionsNeural Information Processing Systems (NeurIPS), 2022

Cristóbal Guzmán

Jelena Diakonikolas

311

14

0

17 Mar 2022

Don't fear the unlabelled: safe semi-supervised learning via simple
debiasing

Don't fear the unlabelled: safe semi-supervised learning via simple debiasingInternational Conference on Learning Representations (ICLR), 2022

Pierre-Alexandre Mattei

280

14

0

14 Mar 2022

Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched
Gradients

Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients

182

2

0

14 Mar 2022

Fast Gradient Methods for Data-Consistent Local Super-Resolution of Medical Images

Fast Gradient Methods for Data-Consistent Local Super-Resolution of Medical Images

370

0

0

22 Feb 2022

MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an
Exponential Convergence Rate

MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate

186

0

0

21 Feb 2022

Policy Learning and Evaluation with Randomized Quasi-Monte Carlo

Policy Learning and Evaluation with Randomized Quasi-Monte CarloInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Sébastien M. R. Arnold

Yi-fan Chen

180

4

0

16 Feb 2022

Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient
Methods

Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient MethodsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Aleksandr Beznosikov

Eduard A. Gorbunov

332

58

0

15 Feb 2022

Equivariance Regularization for Image Reconstruction

Equivariance Regularization for Image Reconstruction

201

3

0

10 Feb 2022

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