Cover Image

Generalized EXTRA stochastic gradient Langevin dynamics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gurbuzbalaban, Mert, Islam, Mohammad Rafiqul, Wang, Xiaoyu, Zhu, Lingjiong
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DIGing--SGLD: Decentralized and Scalable Langevin Sampling over Time--Varying Networks
by: Bajwa, Waheed U., et al.
Published: (2025)

Heavy-Tail Phenomenon in Decentralized SGD
by: Gurbuzbalaban, Mert, et al.
Published: (2022)

High-Order Langevin Monte Carlo Algorithms
by: Dang, Thanh, et al.
Published: (2025)

Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy-Tailed Noise
by: Dang, Thanh, et al.
Published: (2025)

Accelerated Gradient Methods with Biased Gradient Estimates: Risk Sensitivity, High-Probability Guarantees, and Large Deviation Bounds
by: Gürbüzbalaban, Mert, et al.
Published: (2025)

Decentralized Proximal Stochastic Gradient Langevin Dynamics
by: Islam, Mohammad Rafiqul, et al.
Published: (2026)

Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling
by: Gürbüzbalaban, Mert, et al.
Published: (2022)

RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
by: Fang, Cheng, et al.
Published: (2025)

Anchored Langevin Algorithms
by: Gurbuzbalaban, Mert, et al.
Published: (2025)

Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
by: Lim, Dong-Young, et al.
Published: (2022)

Almost sure convergence rates of stochastic gradient methods under gradient domination
by: Weissmann, Simon, et al.
Published: (2024)

A stochastic gradient method for trilevel optimization
by: Giovannelli, Tommaso, et al.
Published: (2025)

Dealing with unbounded gradients in stochastic saddle-point optimization
by: Neu, Gergely, et al.
Published: (2024)

New logarithmic step size for stochastic gradient descent
by: Shamaee, M. Soheil, et al.
Published: (2024)

Constrained Sampling with Primal-Dual Langevin Monte Carlo
by: Chamon, Luiz F. O., et al.
Published: (2024)

Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization
by: Le, Tam
Published: (2025)

Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization
by: Zhang, Fangzhao, et al.
Published: (2024)

Convergence of continuous-time stochastic gradient descent with applications to deep neural networks
by: Lugosi, Gabor, et al.
Published: (2024)

Convergence rates of stochastic gradient method with independent sequences of step-size and momentum weight
by: Hwang, Wen-Liang
Published: (2024)

A stochastic gradient descent algorithm with random search directions
by: Gbaguidi, Eméric
Published: (2025)

Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes
by: Lan, Guanghui, et al.
Published: (2024)

Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks
by: An, Jing, et al.
Published: (2023)

Mirror Mean-Field Langevin Dynamics
by: Gu, Anming, et al.
Published: (2025)

The generator gradient estimator is an adjoint state method for stochastic differential equations
by: Badolle, Quentin, et al.
Published: (2024)

Stochastic Gradient Langevin Dynamics with Variance Reduction
by: Huang, Zhishen, et al.
Published: (2021)

SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems
by: Zhang, Xuan, et al.
Published: (2022)

Robust Accelerated Primal-Dual Methods for Computing Saddle Points
by: Zhang, Xuan, et al.
Published: (2021)

An Accelerated Primal Dual Algorithm with Backtracking for Decentralized Constrained Optimization
by: Xu, Qiushui, et al.
Published: (2025)

Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach
by: Wang, Guillaume, et al.
Published: (2024)

On the stability of gradient descent with second order dynamics for time-varying cost functions
by: Gibson, Travis E., et al.
Published: (2024)

Stability properties of gradient flow dynamics for the symmetric low-rank matrix factorization problem
by: Mohammadi, Hesameddin, et al.
Published: (2024)

Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks
by: Jentzen, Arnulf, et al.
Published: (2024)

Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time
by: Kim, Sungyoon, et al.
Published: (2024)

Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
by: Kim, Juno, et al.
Published: (2023)

Convergence of Stochastic Gradient Langevin Dynamics in the Lazy Training Regime
by: Oberweis, Noah, et al.
Published: (2025)

Error dynamics of mini-batch gradient descent with random reshuffling for least squares regression
by: Lok, Jackie, et al.
Published: (2024)

Proximal Interacting Particle Langevin Algorithms
by: Encinar, Paula Cordero, et al.
Published: (2024)

Langevin Dynamics: A Unified Perspective on Optimization via Lyapunov Potentials
by: Chen, August Y., et al.
Published: (2024)

Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence
by: Zhang, Shiyuan, et al.
Published: (2026)

A Recovery Guarantee for Sparse Neural Networks
by: Fridovich-Keil, Sara, et al.
Published: (2025)