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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
Aaron Defazio
Francis R. Bach
Damien Scieur
    ODL
ArXiv (abs)PDFHTML

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

50 / 878 papers shown
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo
Xingrun Yan
Rongfei Fan
Li Shen
P. Zhao
Jie Xu
Han Hu
FedML
400
3
0
18 Aug 2024
Ordered Momentum for Asynchronous SGD
Ordered Momentum for Asynchronous SGDNeural Information Processing Systems (NeurIPS), 2024
Chang-Wei Shi
Yi-Rui Yang
Wu-Jun Li
ODL
433
2
0
27 Jul 2024
Stochastic Variance-Reduced Iterative Hard Thresholding in Graph
  Sparsity Optimization
Stochastic Variance-Reduced Iterative Hard Thresholding in Graph Sparsity Optimization
Derek Fox
Samuel Hernandez
Qianqian Tong
237
0
0
24 Jul 2024
SOREL: A Stochastic Algorithm for Spectral Risks Minimization
SOREL: A Stochastic Algorithm for Spectral Risks Minimization
Yuze Ge
Rujun Jiang
218
0
0
19 Jul 2024
Enhancing Stochastic Optimization for Statistical Efficiency Using
  ROOT-SGD with Diminishing Stepsize
Enhancing Stochastic Optimization for Statistical Efficiency Using ROOT-SGD with Diminishing Stepsize
Tong Zhang
Chris Junchi Li
322
0
0
15 Jul 2024
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Pratik Rathore
Zachary Frangella
Madeleine Udell
Michał Dereziński
Madeleine Udell
309
0
0
14 Jul 2024
Stabilized Proximal-Point Methods for Federated Optimization
Stabilized Proximal-Point Methods for Federated Optimization
Xiaowen Jiang
Anton Rodomanov
Sebastian U. Stich
FedML
292
8
0
09 Jul 2024
Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
Hoang Tran
Qinzi Zhang
Ashok Cutkosky
347
4
0
01 Jul 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
206
2
0
21 Jun 2024
ModSec-Learn: Boosting ModSecurity with Machine Learning
ModSec-Learn: Boosting ModSecurity with Machine Learning
Christian Scano
Giuseppe Floris
Giuseppe Floris
Christian Scano
Biagio Montaruli
Luca Demetrio
Andrea Valenza
Luca Piras
Davide Balzarotti
Battista Biggio
AAML
109
5
0
19 Jun 2024
Efficient Continual Finite-Sum Minimization
Efficient Continual Finite-Sum MinimizationInternational Conference on Learning Representations (ICLR), 2024
Ioannis Mavrothalassitis
Stratis Skoulakis
L. Dadi
Volkan Cevher
229
0
0
07 Jun 2024
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization
Quantum Algorithms and Lower Bounds for Finite-Sum Optimization
Yexin Zhang
Chenyi Zhang
Cong Fang
Liwei Wang
Tongyang Li
169
4
0
05 Jun 2024
Efficient Sign-Based Optimization: Accelerating Convergence via Variance
  Reduction
Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction
Wei Jiang
Sifan Yang
Wenhao Yang
Lijun Zhang
267
11
0
01 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
271
2
0
27 May 2024
A Unified Theory of Stochastic Proximal Point Methods without Smoothness
A Unified Theory of Stochastic Proximal Point Methods without Smoothness
Peter Richtárik
Abdurakhmon Sadiev
Yury Demidovich
233
6
0
24 May 2024
Exact Gauss-Newton Optimization for Training Deep Neural Networks
Exact Gauss-Newton Optimization for Training Deep Neural Networks
Mikalai Korbit
Adeyemi Damilare Adeoye
Alberto Bemporad
Mario Zanon
ODL
284
6
0
23 May 2024
FedStale: leveraging stale client updates in federated learning
FedStale: leveraging stale client updates in federated learning
Angelo Rodio
Giovanni Neglia
FedML
232
10
0
07 May 2024
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient
  Push with Tight Utility Bounds
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility BoundsInternational Joint Conference on Artificial Intelligence (IJCAI), 2024
Zehan Zhu
Yan Huang
Xin Wang
Jinming Xu
203
6
0
04 May 2024
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Tanmay Gautam
Youngsuk Park
Hao Zhou
Parameswaran Raman
Wooseok Ha
308
35
0
11 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
220
3
0
08 Apr 2024
A Stochastic Quasi-Newton Method for Non-convex Optimization with
  Non-uniform Smoothness
A Stochastic Quasi-Newton Method for Non-convex Optimization with Non-uniform SmoothnessIEEE Conference on Decision and Control (CDC), 2024
Zhenyu Sun
Ermin Wei
315
1
0
22 Mar 2024
Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
Shiyuan Zuo
Xingrun Yan
Rongfei Fan
Han Hu
Hangguan Shan
Tony Q.S. Quek
Puning Zhao
AAMLFedML
505
6
0
20 Mar 2024
A Selective Review on Statistical Methods for Massive Data Computation:
  Distributed Computing, Subsampling, and Minibatch Techniques
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
Xuetong Li
Yuan Gao
Hong Chang
Danyang Huang
Yingying Ma
...
Ke Xu
Jing Zhou
Xuening Zhu
Yingqiu Zhu
Hansheng Wang
202
17
0
17 Mar 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
240
3
0
11 Mar 2024
Shuffling Momentum Gradient Algorithm for Convex Optimization
Shuffling Momentum Gradient Algorithm for Convex Optimization
Trang H. Tran
Quoc Tran-Dinh
Lam M. Nguyen
219
2
0
05 Mar 2024
CausalGym: Benchmarking causal interpretability methods on linguistic
  tasks
CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Aryaman Arora
Daniel Jurafsky
Christopher Potts
181
33
0
19 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
193
1
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
257
3
0
04 Feb 2024
Decentralized Sum-of-Nonconvex Optimization
Decentralized Sum-of-Nonconvex Optimization
Zhuanghua Liu
K. H. Low
159
0
0
04 Feb 2024
Step-size Optimization for Continual Learning
Step-size Optimization for Continual Learning
T. Degris
Khurram Javed
Arsalan Sharifnassab
Yuxin Liu
Richard Sutton
160
5
0
30 Jan 2024
Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex
  Finite Sum Problems
Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum ProblemsPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2024
Yanjie Zhong
Jiaqi Li
Soumendra Lahiri
226
2
0
29 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
287
4
0
18 Jan 2024
Understanding the Role of Optimization in Double Descent
Understanding the Role of Optimization in Double Descent
Chris Yuhao Liu
Jeffrey Flanigan
202
0
0
06 Dec 2023
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
Frederik Köhne
Leonie Kreis
Anton Schiela
Roland A. Herzog
267
2
0
28 Nov 2023
On the Communication Complexity of Decentralized Bilevel Optimization
On the Communication Complexity of Decentralized Bilevel Optimization
Yihan Zhang
My T. Thai
Jie Wu
Hongchang Gao
473
6
0
19 Nov 2023
Efficiently Escaping Saddle Points for Policy Optimization
Efficiently Escaping Saddle Points for Policy OptimizationConference on Uncertainty in Artificial Intelligence (UAI), 2023
Sadegh Khorasani
Saber Salehkaleybar
Negar Kiyavash
Niao He
Matthias Grossglauser
265
1
0
15 Nov 2023
A Coefficient Makes SVRG Effective
A Coefficient Makes SVRG Effective
Yida Yin
Zhiqiu Xu
Zhiyuan Li
Trevor Darrell
Zhuang Liu
318
5
0
09 Nov 2023
Computing Approximate $\ell_p$ Sensitivities
Computing Approximate ℓp\ell_pℓp​ Sensitivities
Swati Padmanabhan
David P. Woodruff
Qiuyi Zhang
255
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
667
0
0
06 Nov 2023
A Variational Perspective on High-Resolution ODEs
A Variational Perspective on High-Resolution ODEsNeural Information Processing Systems (NeurIPS), 2023
Hoomaan Maskan
K. C. Zygalakis
A. Yurtsever
303
5
0
03 Nov 2023
Balancing Act: Constraining Disparate Impact in Sparse Models
Balancing Act: Constraining Disparate Impact in Sparse ModelsInternational Conference on Learning Representations (ICLR), 2023
Meraj Hashemizadeh
Juan Ramirez
Rohan Sukumaran
G. Farnadi
Damien Scieur
Jose Gallego-Posada
319
9
0
31 Oct 2023
Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and
  Exp-Concave Games with Gradient Feedback
Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient FeedbackOperational Research (OR), 2023
Michael I. Jordan
Tianyi Lin
Zhengyuan Zhou
527
7
0
21 Oct 2023
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency
  for Federated Learning with Static and Streaming Dataset
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Weijie Liu
Xiaoxi Zhang
Jingpu Duan
Carlee Joe-Wong
Zhi Zhou
Xu Chen
238
21
0
20 Oct 2023
Stochastic Optimization for Non-convex Problem with Inexact Hessian
  Matrix, Gradient, and Function
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and FunctionIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023
Liu Liu
Xuanqing Liu
Cho-Jui Hsieh
Dacheng Tao
155
5
0
18 Oct 2023
Communication Compression for Byzantine Robust Learning: New Efficient
  Algorithms and Improved Rates
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
Ahmad Rammal
Kaja Gruntkowska
Nikita Fedin
Eduard A. Gorbunov
Peter Richtárik
338
11
0
15 Oct 2023
Variance-Reduced Stochastic Optimization for Efficient Inference of
  Hidden Markov Models
Variance-Reduced Stochastic Optimization for Efficient Inference of Hidden Markov ModelsJournal of Computational And Graphical Statistics (JCGS), 2023
E. Sidrow
Nancy Heckman
Alexandre Bouchard-Coté
S. Fortune
A. Trites
M. Auger-Méthé
315
1
0
06 Oct 2023
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions
Variance Reduced Halpern Iteration for Finite-Sum Monotone InclusionsInternational Conference on Learning Representations (ICLR), 2023
Xu Cai
Ahmet Alacaoglu
Jelena Diakonikolas
326
12
0
04 Oct 2023
Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced
  Variance
Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced Variance
Dun Zeng
Zenglin Xu
Yu Pan
Xu Luo
Qifan Wang
Xiaoying Tang
FedML
269
1
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
223
0
0
03 Oct 2023
Robust Stochastic Optimization via Gradient Quantile Clipping
Robust Stochastic Optimization via Gradient Quantile Clipping
Ibrahim Merad
Stéphane Gaïffas
195
3
0
29 Sep 2023
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