Guardado en:
| Autores principales: | Takezawa, Yuki, Koloskova, Anastasia, Jiang, Xiaowen, Stich, Sebastian U. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.26337 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
por: Takezawa, Yuki, et al.
Publicado: (2025)
por: Takezawa, Yuki, et al.
Publicado: (2025)
Scalable Decentralized Learning with Teleportation
por: Takezawa, Yuki, et al.
Publicado: (2025)
por: Takezawa, Yuki, et al.
Publicado: (2025)
On Convergence of Incremental Gradient for Non-Convex Smooth Functions
por: Koloskova, Anastasia, et al.
Publicado: (2023)
por: Koloskova, Anastasia, et al.
Publicado: (2023)
Federated Optimization with Doubly Regularized Drift Correction
por: Jiang, Xiaowen, et al.
Publicado: (2024)
por: Jiang, Xiaowen, et al.
Publicado: (2024)
Stabilized Proximal-Point Methods for Federated Optimization
por: Jiang, Xiaowen, et al.
Publicado: (2024)
por: Jiang, Xiaowen, et al.
Publicado: (2024)
Non-Convex Federated Optimization under Cost-Aware Client Selection
por: Jiang, Xiaowen, et al.
Publicado: (2025)
por: Jiang, Xiaowen, et al.
Publicado: (2025)
Locally Adaptive Federated Learning
por: Mukherjee, Sohom, et al.
Publicado: (2023)
por: Mukherjee, Sohom, et al.
Publicado: (2023)
Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation
por: Otsuka, Kaoru, et al.
Publicado: (2025)
por: Otsuka, Kaoru, et al.
Publicado: (2025)
Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs)
por: Riabinin, Artem, et al.
Publicado: (2025)
por: Riabinin, Artem, et al.
Publicado: (2025)
Non-convex Stochastic Composite Optimization with Polyak Momentum
por: Gao, Yuan, et al.
Publicado: (2024)
por: Gao, Yuan, et al.
Publicado: (2024)
Accelerated Distributed Optimization with Compression and Error Feedback
por: Gao, Yuan, et al.
Publicado: (2025)
por: Gao, Yuan, et al.
Publicado: (2025)
Towards Faster Decentralized Stochastic Optimization with Communication Compression
por: Islamov, Rustem, et al.
Publicado: (2024)
por: Islamov, Rustem, et al.
Publicado: (2024)
Avoiding Bias in Clipped SGD for Overparameterized Models under Generalized Smoothness
por: Lobanov, Aleksandr, et al.
Publicado: (2026)
por: Lobanov, Aleksandr, et al.
Publicado: (2026)
Better LMO-based Momentum Methods with Second-Order Information
por: Khirirat, Sarit, et al.
Publicado: (2025)
por: Khirirat, Sarit, et al.
Publicado: (2025)
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
por: Zhang, Siqi, et al.
Publicado: (2023)
por: Zhang, Siqi, et al.
Publicado: (2023)
Composite Optimization with Error Feedback: the Dual Averaging Approach
por: Gao, Yuan, et al.
Publicado: (2025)
por: Gao, Yuan, et al.
Publicado: (2025)
Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
por: Rodomanov, Anton, et al.
Publicado: (2024)
por: Rodomanov, Anton, et al.
Publicado: (2024)
FedMuon: Accelerating Federated Learning with Matrix Orthogonalization
por: Liu, Junkang, et al.
Publicado: (2025)
por: Liu, Junkang, et al.
Publicado: (2025)
Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle
por: Richtárik, Peter, et al.
Publicado: (2026)
por: Richtárik, Peter, et al.
Publicado: (2026)
Parameter-free Clipped Gradient Descent Meets Polyak
por: Takezawa, Yuki, et al.
Publicado: (2024)
por: Takezawa, Yuki, et al.
Publicado: (2024)
DADA: Dual Averaging with Distance Adaptation
por: Moshtaghifar, Mohammad, et al.
Publicado: (2025)
por: Moshtaghifar, Mohammad, et al.
Publicado: (2025)
The Privacy Power of Correlated Noise in Decentralized Learning
por: Allouah, Youssef, et al.
Publicado: (2024)
por: Allouah, Youssef, et al.
Publicado: (2024)
LiMuon: Light and Fast Muon Optimizer for Large Models
por: Huang, Feihu, et al.
Publicado: (2025)
por: Huang, Feihu, et al.
Publicado: (2025)
Implicit Bias of Spectral Descent and Muon on Multiclass Separable Data
por: Fan, Chen, et al.
Publicado: (2025)
por: Fan, Chen, et al.
Publicado: (2025)
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
por: Luo, Ruichen, et al.
Publicado: (2025)
por: Luo, Ruichen, et al.
Publicado: (2025)
Accelerated Stochastic Min-Max Optimization Based on Bias-corrected Momentum
por: Cai, Haoyuan, et al.
Publicado: (2024)
por: Cai, Haoyuan, et al.
Publicado: (2024)
FedGiA: An Efficient Hybrid Algorithm for Federated Learning
por: Zhou, Shenglong, et al.
Publicado: (2022)
por: Zhou, Shenglong, et al.
Publicado: (2022)
FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
por: Sahu, Harekrushna, et al.
Publicado: (2026)
por: Sahu, Harekrushna, et al.
Publicado: (2026)
Improved Convergence Rates of Muon Optimizer for Nonconvex Optimization
por: Nagashima, Shuntaro, et al.
Publicado: (2026)
por: Nagashima, Shuntaro, et al.
Publicado: (2026)
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling
por: Chen, Cheng, et al.
Publicado: (2020)
por: Chen, Cheng, et al.
Publicado: (2020)
Muon Optimizes Under Spectral Norm Constraints
por: Chen, Lizhang, et al.
Publicado: (2025)
por: Chen, Lizhang, et al.
Publicado: (2025)
The Newton-Muon Optimizer
por: Du, Zhehang, et al.
Publicado: (2026)
por: Du, Zhehang, et al.
Publicado: (2026)
Lions and Muons: Optimization via Stochastic Frank-Wolfe
por: Sfyraki, Maria-Eleni, et al.
Publicado: (2025)
por: Sfyraki, Maria-Eleni, et al.
Publicado: (2025)
Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method
por: Sadiev, Abdurakhmon, et al.
Publicado: (2026)
por: Sadiev, Abdurakhmon, et al.
Publicado: (2026)
Phases of Muon: When Muon Eclipses SignSGD
por: Paquette, Elliot, et al.
Publicado: (2026)
por: Paquette, Elliot, et al.
Publicado: (2026)
FedSGM: A Unified Framework for Constraint Aware, Bidirectionally Compressed, Multi-Step Federated Optimization
por: Upadhyay, Antesh, et al.
Publicado: (2026)
por: Upadhyay, Antesh, et al.
Publicado: (2026)
FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
por: You, Runze, et al.
Publicado: (2025)
por: You, Runze, et al.
Publicado: (2025)
MiMuon: Mixed Muon Optimizer with Improved Generalization for Large Models
por: Huang, Feihu, et al.
Publicado: (2026)
por: Huang, Feihu, et al.
Publicado: (2026)
MuonBP: Faster Muon via Block-Periodic Orthogonalization
por: Khaled, Ahmed, et al.
Publicado: (2025)
por: Khaled, Ahmed, et al.
Publicado: (2025)
Optimizer-Induced Mode Connectivity: From AdamW to Muon
por: Zhang, Fangzhao, et al.
Publicado: (2026)
por: Zhang, Fangzhao, et al.
Publicado: (2026)
Ejemplares similares
-
Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
por: Takezawa, Yuki, et al.
Publicado: (2025) -
Scalable Decentralized Learning with Teleportation
por: Takezawa, Yuki, et al.
Publicado: (2025) -
On Convergence of Incremental Gradient for Non-Convex Smooth Functions
por: Koloskova, Anastasia, et al.
Publicado: (2023) -
Federated Optimization with Doubly Regularized Drift Correction
por: Jiang, Xiaowen, et al.
Publicado: (2024) -
Stabilized Proximal-Point Methods for Federated Optimization
por: Jiang, Xiaowen, et al.
Publicado: (2024)