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. 2303.02749
  4. Cited By
Revisiting the Noise Model of Stochastic Gradient Descent

Revisiting the Noise Model of Stochastic Gradient Descent

International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
5 March 2023
Barak Battash
Ofir Lindenbaum
ArXiv (abs)PDFHTML

Papers citing "Revisiting the Noise Model of Stochastic Gradient Descent"

13 / 13 papers shown
Federated Stochastic Minimax Optimization under Heavy-Tailed Noises
Federated Stochastic Minimax Optimization under Heavy-Tailed Noises
Xinwen Zhang
Hongchang Gao
FedML
388
0
0
06 Nov 2025
Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
Abdurakhmon Sadiev
Peter Richtárik
Ilyas Fatkhullin
145
2
0
12 Oct 2025
TempoControl: Temporal Attention Guidance for Text-to-Video Models
TempoControl: Temporal Attention Guidance for Text-to-Video Models
Shira Schiber
Ofir Lindenbaum
Idan Schwartz
DiffMVGen
307
0
0
02 Oct 2025
Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noises
Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noises
Xinwen Zhang
Yihan Zhang
Hongchang Gao
119
1
0
19 Sep 2025
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
Tao Shen
Zexi Li
Didi Zhu
Ziyu Zhao
Chao-Xiang Wu
Fei Wu
FedML
162
0
0
20 Aug 2025
Can SGD Handle Heavy-Tailed Noise?
Can SGD Handle Heavy-Tailed Noise?
Ilyas Fatkhullin
Florian Hübler
Guanghui Lan
128
5
0
06 Aug 2025
SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training
SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training
Yehonathan Refael
Guy Smorodinsky
Tom Tirer
Ofir Lindenbaum
181
5
0
30 May 2025
Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent
Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent
Max Hennick
Stijn De Baerdemacker
229
3
0
28 Mar 2025
AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-TuningInternational Conference on Learning Representations (ICLR), 2024
Yehonathan Refael
Jonathan Svirsky
Boris Shustin
Wasim Huleihel
Ofir Lindenbaum
300
10
0
31 Dec 2024
From Gradient Clipping to Normalization for Heavy Tailed SGD
From Gradient Clipping to Normalization for Heavy Tailed SGDInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Florian Hübler
Ilyas Fatkhullin
Niao He
404
29
0
17 Oct 2024
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
Aleksandar Armacki
Shuhua Yu
Pranay Sharma
Gauri Joshi
Dragana Bajović
D. Jakovetić
S. Kar
409
2
0
17 Oct 2024
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution
Naoki Yoshida
Shogo H. Nakakita
Masaaki Imaizumi
259
1
0
23 Jun 2024
Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
Siyuan Yu
Wei Chen
H. V. Poor
357
0
0
17 Jun 2024
1