Cover Image

In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Liu, Zijian
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping
by: Liu, Zijian, et al.
Published: (2024)

Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad
by: Liu, Zijian
Published: (2026)

Clipped Gradient Methods for Nonsmooth Convex Optimization under Heavy-Tailed Noise: A Refined Analysis
by: Liu, Zijian
Published: (2025)

Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
by: Pan, Rui, et al.
Published: (2023)

Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
by: Liu, Zijian, et al.
Published: (2023)

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

Optimal Asynchronous Stochastic Nonconvex Optimization under Heavy-Tailed Noise
by: Wu, Yidong, et al.
Published: (2026)

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2023)

On the Last-Iterate Convergence of Shuffling Gradient Methods
by: Liu, Zijian, et al.
Published: (2024)

Near-Optimal Decentralized Stochastic Nonconvex Optimization with Heavy-Tailed Noise
by: Wang, Menglian, et al.
Published: (2026)

Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration
by: Sun, Tao, et al.
Published: (2024)

Muon Converges under Heavy-Tailed Noise: Nonconvex Hölder-Smooth Empirical Risk Minimization
by: Iiduka, Hideaki
Published: (2026)

Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
by: Zhu, Tianxi, et al.
Published: (2025)

Online Convex Optimization with Heavy Tails: Old Algorithms, New Regrets, and Applications
by: Liu, Zijian
Published: (2025)

Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
by: Liu, Zijian, et al.
Published: (2025)

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
by: Agrawal, Shubhada, et al.
Published: (2026)

High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2021)

Can SGD Handle Heavy-Tailed Noise?
by: Fatkhullin, Ilyas, et al.
Published: (2025)

Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise
by: Chezhegov, Savelii, et al.
Published: (2025)

High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
by: Armacki, Aleksandar, et al.
Published: (2023)

Heavy-Tailed and Long-Range Dependent Noise in Stochastic Approximation: A Finite-Time Analysis
by: Chandak, Siddharth, et al.
Published: (2026)

Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods
by: Mukherjee, Amartya, et al.
Published: (2025)

An Improved Analysis of the Clipped Stochastic subGradient Method under Heavy-Tailed Noise
by: Parletta, Daniela Angela, et al.
Published: (2024)

Sharp High-Probability Rates for Nonlinear SGD under Heavy-Tailed Noise via Symmetrization
by: Armacki, Aleksandar, et al.
Published: (2025)

Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
by: Sadiev, Abdurakhmon, et al.
Published: (2025)

From Gradient Clipping to Normalization for Heavy Tailed SGD
by: Hübler, Florian, et al.
Published: (2024)

Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation
by: Mishkin, Aaron, et al.
Published: (2024)

Why is Normalization Preferred? A Worst-Case Complexity Theory for Stochastically Preconditioned SGD under Heavy-Tailed Noise
by: Fang, Yuchen, et al.
Published: (2026)

Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
by: Klein, Sara, et al.
Published: (2023)

Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
by: Armacki, Aleksandar, et al.
Published: (2024)

Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise
by: Zhang, Jiayu, et al.
Published: (2026)

Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition
by: Choudhury, Sayantan, et al.
Published: (2026)

Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems
by: Puchkin, Nikita, et al.
Published: (2023)

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2024)

(Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum
by: Dang, Anh, et al.
Published: (2024)

Momentum Does Not Reduce Stochastic Noise in Stochastic Gradient Descent
by: Sato, Naoki, et al.
Published: (2024)

Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
by: Chezhegov, Savelii, et al.
Published: (2024)

Distributed Stochastic Optimization under Heavy-Tailed Noises
by: Sun, Chao, et al.
Published: (2023)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)

Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise
by: Yu, Dingzhi, et al.
Published: (2026)