Cover Image

Information-Theoretic Generalization Bounds for Stochastic Gradient Descent with Predictable Virtual Noise

Fuente: arXiv

Saved in:

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

Similar Items

More Optimal Fractional-Order Stochastic Gradient Descent for Non-Convex Optimization Problems
by: Partohaghighi, Mohammad, et al.
Published: (2025)

Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems
by: Partohaghighi, Mohammad, et al.
Published: (2025)

A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent
by: Wang, Mingze, et al.
Published: (2023)

Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
by: Ma, Wenquan, et al.
Published: (2026)

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

Noise Balance and Stationary Distribution of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2023)

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

On the Generalization of Stochastic Gradient Descent with Momentum
by: Ramezani-Kebrya, Ali, et al.
Published: (2018)

Tight Generalization Error Bounds for Stochastic Gradient Descent in Non-convex Learning
by: Xiong, Wenjun, et al.
Published: (2025)

SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning
by: Partohaghighi, Mohammad, et al.
Published: (2026)

Deep Learning under Fractional-Order Differential Privacy
by: Partohaghighi, Mohammad, et al.
Published: (2026)

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

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
by: Vaswani, Sharan, et al.
Published: (2021)

When Gradient Clipping Becomes a Control Mechanism for Differential Privacy in Deep Learning
by: Partohaghighi, Mohammad, et al.
Published: (2026)

Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)

DOME: Improving Signal-to-Noise in Stochastic Gradient Descent via Sharp-Direction Subspace Filtering
by: Nicolas, Julien, et al.
Published: (2025)

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

Fractional-Order Federated Learning
by: Partohaghighi, Mohammad, et al.
Published: (2026)

Adjacent Leader Decentralized Stochastic Gradient Descent
by: He, Haoze, et al.
Published: (2024)

A Bootstrap Perspective on Stochastic Gradient Descent
by: Lan, Hongjian, et al.
Published: (2025)

Stochastic Gradient Descent for Nonparametric Additive Regression
by: Chen, Xin, et al.
Published: (2024)

Bolstering Stochastic Gradient Descent with Model Building
by: Birbil, S. Ilker, et al.
Published: (2021)

Descend or Rewind? Stochastic Gradient Descent Unlearning
by: Mu, Siqiao, et al.
Published: (2025)

Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study
by: Alexander, Yotam, et al.
Published: (2025)

Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)

Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)

Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay
by: Balik, Mehmet Yigit
Published: (2024)

Personalized Federated Learning with Exact Stochastic Gradient Descent
by: Nikoloutsopoulos, Sotirios, et al.
Published: (2022)

Stochastic Gradient Descent for Two-layer Neural Networks
by: Cao, Dinghao, et al.
Published: (2024)

Stochastic Gradient Descent for Gaussian Processes Done Right
by: Lin, Jihao Andreas, et al.
Published: (2023)

Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024)

Towards Learning Stochastic Population Models by Gradient Descent
by: Kreikemeyer, Justin N., et al.
Published: (2024)

Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent
by: Deng, Xiaoge, et al.
Published: (2023)

Learning Curves of Stochastic Gradient Descent in Kernel Regression
by: Zhang, Haihan, et al.
Published: (2025)

Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
by: Li, Jiaqi, et al.
Published: (2025)

How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?
by: Huang, Wei, et al.
Published: (2025)

Adaptive Batch Sizes Using Non-Euclidean Gradient Noise Scales for Stochastic Sign and Spectral Descent
by: Naganuma, Hiroki, et al.
Published: (2026)

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

Stochastic Gradient Descent with Momentum is Algorithmically Stable
by: Lei, Yunwen, et al.
Published: (2026)

Adaptive Heavy-Tailed Stochastic Gradient Descent
by: Gong, Bodu, et al.
Published: (2025)