Imagen de Portada

Preconditioned Inexact Stochastic ADMM for Deep Model

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhou, Shenglong, Wang, Ouya, Luo, Ziyan, Zhu, Yongxu, Li, Geoffrey Ye
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

BADM: Batch ADMM for Deep Learning
por: Wang, Ouya, et al.
Publicado: (2024)

FedGiA: An Efficient Hybrid Algorithm for Federated Learning
por: Zhou, Shenglong, et al.
Publicado: (2022)

FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning
por: Song, Yongcun, et al.
Publicado: (2024)

Sparse Decentralized Federated Learning
por: Sha, Shan, et al.
Publicado: (2023)

Decentralized Federated Learning by Partial Message Exchange
por: Sha, Shan, et al.
Publicado: (2026)

Bilevel Learning with Inexact Stochastic Gradients
por: Salehi, Mohammad Sadegh, et al.
Publicado: (2024)

Stochastic Gradient Methods with Preconditioned Updates
por: Sadiev, Abdurakhmon, et al.
Publicado: (2022)

A General Continuous-Time Formulation of Stochastic ADMM and Its Variants
por: Li, Chris Junchi
Publicado: (2024)

Memory-Efficient 4-bit Preconditioned Stochastic Optimization
por: Li, Jingyang, et al.
Publicado: (2024)

Fast Adaptation for Deep Learning-based Wireless Communications
por: Wang, Ouya, et al.
Publicado: (2024)

On the Convergence Analysis of an Inexact Preconditioned Stochastic Model-Based Algorithm
por: Bao, Chenglong, et al.
Publicado: (2025)

Stochastic Preconditioning for Neural Field Optimization
por: Ling, Selena, et al.
Publicado: (2025)

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
por: Oikonomidis, Konstantinos, et al.
Publicado: (2026)

Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
por: Köhne, Frederik, et al.
Publicado: (2023)

FedAPM: Federated Learning via ADMM with Partial Model Personalization
por: Zhu, Shengkun, et al.
Publicado: (2025)

Preconditioning for Accelerated Gradient Descent Optimization and Regularization
por: Ye, Qiang
Publicado: (2024)

Contextual Strongly Convex Simulation Optimization: Optimize then Predict with Inexact Solutions
por: Lin, Nifei, et al.
Publicado: (2025)

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning
por: Sun, Jingruo, et al.
Publicado: (2025)

On the Convergence of Single-Loop Stochastic Bilevel Optimization with Approximate Implicit Differentiation
por: Zhou, Yubo, et al.
Publicado: (2026)

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
por: Zhu, Shengkun, et al.
Publicado: (2024)

OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention
por: Zhou, Chenyu, et al.
Publicado: (2026)

Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
por: Alacaoglu, Ahmet, et al.
Publicado: (2024)

Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
por: Ye, Zhenzhang, et al.
Publicado: (2024)

PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
por: Frangella, Zachary, et al.
Publicado: (2023)

Federated Smoothing ADMM for Localization
por: Mirzaeifard, Reza, et al.
Publicado: (2025)

How Does Preconditioning Guide Feature Learning in Deep Neural Networks?
por: Yoshida, Kotaro, et al.
Publicado: (2025)

DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning
por: Bosch, Marc Molina Van den, et al.
Publicado: (2026)

Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment
por: Ahmed, Shibbir, et al.
Publicado: (2024)

Stochastic Two Points Method for Deep Model Zeroth-order Optimization
por: Pang, Yijiang, et al.
Publicado: (2024)

Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces
por: Cheng, Zilan, et al.
Publicado: (2026)

Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
por: Li, Rui, et al.
Publicado: (2024)

Universal Sequence Preconditioning
por: Marsden, Annie, et al.
Publicado: (2025)

A Riemannian ADMM
por: Li, Jiaxiang, et al.
Publicado: (2022)

Rethinking Consistent Multi-Label Classification Under Inexact Supervision
por: Wang, Wei, et al.
Publicado: (2025)

Personalized Federated Learning via ADMM with Moreau Envelope
por: Zhu, Shengkun, et al.
Publicado: (2023)

AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks
por: Ebrahimi, Zeinab, et al.
Publicado: (2024)

SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference
por: Ma, Hao, et al.
Publicado: (2026)

LUCID: Attention with Preconditioned Representations
por: Duvvuri, Sai Surya, et al.
Publicado: (2026)

Preconditioning for Physics-Informed Neural Networks
por: Liu, Songming, et al.
Publicado: (2024)

Distributionally Robust Federated Learning: An ADMM Algorithm
por: Bai, Wen, et al.
Publicado: (2025)