ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1407.0202
  4. 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
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 / 881 papers shown
Non-Convex Federated Optimization under Cost-Aware Client Selection
Non-Convex Federated Optimization under Cost-Aware Client Selection
Xiaowen Jiang
Anton Rodomanov
Sebastian U. Stich
FedML
324
0
0
05 Dec 2025
Variance Matters: Improving Domain Adaptation via Stratified Sampling
Variance Matters: Improving Domain Adaptation via Stratified Sampling
Andrea Napoli
Paul White
42
0
0
04 Dec 2025
Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
Accelerated Distributional Temporal Difference Learning with Linear Function Approximation
Kaicheng Jin
Yang Peng
Jiansheng Yang
Zhihua Zhang
91
0
0
16 Nov 2025
Sampling and Loss Weights in Multi-Domain Training
Sampling and Loss Weights in Multi-Domain Training
Mahdi Salmani
Pratik Worah
Meisam Razaviyayn
Vahab Mirrokni
NoLa
358
0
0
10 Nov 2025
Structured Matrix Scaling for Multi-Class Calibration
Structured Matrix Scaling for Multi-Class Calibration
Eugene Berta
David Holzmüller
Michael I. Jordan
Francis Bach
191
4
0
05 Nov 2025
Convergence Analysis of SGD under Expected Smoothness
Convergence Analysis of SGD under Expected Smoothness
Yuta Kawamoto
Hideaki Iiduka
187
0
0
23 Oct 2025
On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
Shaocong Ma
Heng Huang
188
3
0
22 Oct 2025
MARS-M: When Variance Reduction Meets Matrices
MARS-M: When Variance Reduction Meets Matrices
Yifeng Liu
Angela Yuan
Q. Gu
341
5
0
20 Oct 2025
Personalized Collaborative Learning with Affinity-Based Variance Reduction
Personalized Collaborative Learning with Affinity-Based Variance Reduction
Chenyu Zhang
Navid Azizan
162
0
0
17 Oct 2025
The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas
The Cognitive Bandwidth Bottleneck: Shifting Long-Horizon Agent from Planning with Actions to Planning with Schemas
Baixuan Xu
Tianshi Zheng
Zhaowei Wang
Hong Ting Tsang
Weiqi Wang
Tianqing Fang
Yangqiu Song
189
0
0
08 Oct 2025
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
Shiyuan Zuo
Rongfei Fan
Cheng Zhan
Jie Xu
P. Zhao
Han Hu
AAML
161
0
0
29 Sep 2025
SPRINT: Stochastic Performative Prediction With Variance Reduction
SPRINT: Stochastic Performative Prediction With Variance Reduction
Tian Xie
Ding Zhu
Jia Liu
Mahdi Khalili
X. Zhang
231
1
0
22 Sep 2025
Do Natural Language Descriptions of Model Activations Convey Privileged Information?
Do Natural Language Descriptions of Model Activations Convey Privileged Information?
Millicent Li
Alberto Mario Ceballos Arroyo
Giordano Rogers
Naomi Saphra
Byron C. Wallace
273
4
0
16 Sep 2025
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
Daniil Medyakov
Gleb Molodtsov
Grigoriy Evseev
Egor Petrov
Aleksandr Beznosikov
418
3
0
04 Sep 2025
A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
Xinli Shi
Xingxing Yuan
Longkang Zhu
G. Wen
144
0
0
28 Aug 2025
Stochastic Gradient Descent with Strategic Querying
Stochastic Gradient Descent with Strategic Querying
Nanfei Jiang
Hoi-To Wai
M. Alizadeh
165
0
0
23 Aug 2025
Jointly Computation- and Communication-Efficient Distributed Learning
Jointly Computation- and Communication-Efficient Distributed Learning
Xiaoxing Ren
Nicola Bastianello
Karl H. Johansson
Thomas Parisini
FedML
428
1
0
21 Aug 2025
Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach
Detecting COPD Through Speech Analysis: A Dataset of Danish Speech and Machine Learning Approach
Cuno Sankey-Olsen
Rasmus Hvass Olesen
Tobias Oliver Eberhard
Andreas Triantafyllopoulos
B. Schuller
Ilhan Aslan
146
0
0
04 Aug 2025
EMA Without the Lag: Bias-Corrected Iterate Averaging Schemes
EMA Without the Lag: Bias-Corrected Iterate Averaging Schemes
Adam Block
Cyril Zhang
205
1
0
31 Jul 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
216
0
0
14 Jun 2025
NDCG-Consistent Softmax Approximation with Accelerated Convergence
NDCG-Consistent Softmax Approximation with Accelerated Convergence
Yuanhao Pu
Defu Lian
Xiaolong Chen
Xu Huang
Jin Chen
Enhong Chen
258
0
0
11 Jun 2025
Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order
Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order
Egor Petrov
Grigoriy Evseev
Aleksey Antonov
Andrey Veprikov
Nikolay Bushkov
Nikolay Bushkov
Stanislav Moiseev
529
5
0
04 Jun 2025
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
Wei Zhang
Arif Hassan Zidan
Arif Hassan Zidan
Wei Zhang
Tianming Liu
ODL
306
0
0
16 May 2025
Permutation Randomization on Nonsmooth Nonconvex Optimization: A Theoretical and Experimental Study
Permutation Randomization on Nonsmooth Nonconvex Optimization: A Theoretical and Experimental Study
Wei Zhang
Arif Hassan Zidan
Arif Hassan Zidan
Wei Zhang
Tianming Liu
226
0
0
16 May 2025
Personalized Federated Learning under Model Dissimilarity Constraints
Personalized Federated Learning under Model Dissimilarity Constraints
Samuel Erickson
Mikael Johansson
FedML
768
0
0
12 May 2025
Streaming Krylov-Accelerated Stochastic Gradient Descent
Streaming Krylov-Accelerated Stochastic Gradient Descent
Stephen Thomas
151
0
0
11 May 2025
Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL
Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL
Jiarui Yao
Yifan Hao
Hanning Zhang
Hanze Dong
Wei Xiong
Nan Jiang
Tong Zhang
LRM
527
19
0
05 May 2025
A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression
A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile RegressionMeasurement and Modeling of Computer Systems (SIGMETRICS), 2025
Yixuan Zhang
Dongyan
Yudong Chen
Qiaomin Xie
365
2
0
11 Apr 2025
Node Embeddings via Neighbor Embeddings
Node Embeddings via Neighbor Embeddings
Jan Niklas Böhm
Marius Keute
Alica Guzmán
Sebastian Damrich
Andrew Draganov
D. Kobak
GNN
397
1
0
31 Mar 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
345
1
0
21 Mar 2025
Convergence Analysis of alpha-SVRG under Strong Convexity
Convergence Analysis of alpha-SVRG under Strong ConvexityIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025
Sean Xiao
Sangwoo Park
Stefan Vlaski
296
0
0
16 Mar 2025
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
Xue Feng
M. Paul Laiu
Thomas Strohmer
FedML
257
1
0
14 Mar 2025
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
Daniil Medyakov
Gleb Molodtsov
S. Chezhegov
Alexey Rebrikov
Aleksandr Beznosikov
590
1
0
20 Feb 2025
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning
Jingruo Sun
Zachary Frangella
Madeleine Udell
308
3
0
28 Jan 2025
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing AnalysisInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025
Ruichen Luo
Sebastian U Stich
Samuel Horváth
Martin Takáč
638
2
0
08 Jan 2025
Accelerated Methods with Compressed Communications for Distributed
  Optimization Problems under Data Similarity
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data SimilarityAAAI Conference on Artificial Intelligence (AAAI), 2024
Dmitry Bylinkin
Aleksandr Beznosikov
516
3
0
21 Dec 2024
Analysis of regularized federated learning
Analysis of regularized federated learning
Langming Liu
Dingxuan Zhou
FedML
179
4
0
03 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
257
0
0
01 Nov 2024
Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
Tao Sun
Xinwang Liu
Kun Yuan
392
0
0
21 Oct 2024
Efficient Optimization Algorithms for Linear Adversarial Training
Efficient Optimization Algorithms for Linear Adversarial TrainingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Antônio H. Ribeiro
Thomas B. Schon
Dave Zahariah
Francis Bach
AAML
606
3
0
16 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
306
2
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
265
1
0
09 Oct 2024
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg ExtrapolationInternational Conference on Learning Representations (ICLR), 2024
Marina Sheshukova
Denis Belomestny
Alain Durmus
Eric Moulines
Alexey Naumov
S. Samsonov
388
6
0
07 Oct 2024
Obtaining Lower Query Complexities through Lightweight Zeroth-Order
  Proximal Gradient Algorithms
Obtaining Lower Query Complexities through Lightweight Zeroth-Order Proximal Gradient AlgorithmsNeural Computation (Neural Comput.), 2024
Bin Gu
Xiyuan Wei
Hualin Zhang
Yi Chang
Heng-Chiao Huang
FedML
238
0
0
03 Oct 2024
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifoldInternational Conference on Learning Representations (ICLR), 2024
Hoang Phuc Hau Luu
Hanlin Yu
Bernardo Williams
Marcelo Hartmann
Arto Klami
DRL
461
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
287
1
0
02 Oct 2024
Debiasing Federated Learning with Correlated Client Participation
Debiasing Federated Learning with Correlated Client ParticipationInternational Conference on Learning Representations (ICLR), 2024
Zhenyu Sun
Ziyang Zhang
Zheng Xu
Gauri Joshi
Pranay Sharma
Ermin Wei
FedML
347
1
0
02 Oct 2024
Decentralized Federated Learning with Gradient Tracking over
  Time-Varying Directed Networks
Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks
Duong Thuy Anh Nguyen
Su Wang
Duong Tung Nguyen
Angelia Nedich
H. Vincent Poor
361
3
0
25 Sep 2024
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient
  Similarity to Reduce Communication in Distributed and Federated Learning
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated LearningУспехи математических наук (Uspekhi Mat. Nauk.), 2024
Dmitry Bylinkin
Kirill Degtyarev
Aleksandr Beznosikov
FedML
277
0
0
22 Sep 2024
Improving Tree Probability Estimation with Stochastic Optimization and
  Variance Reduction
Improving Tree Probability Estimation with Stochastic Optimization and Variance ReductionStatistics and computing (Stat. Comput.), 2024
Tianyu Xie
Musu Yuan
Minghua Deng
Cheng Zhang
220
3
0
09 Sep 2024
1234...161718
Next
Page 1 of 18
Pageof 18