:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Jiaqi, Lou, Zhipeng, Schmidt-Hieber, Johannes, Wu, Wei Biao
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2510.12013
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
by: Li, Jiaqi, et al.
Published: (2024)

Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
by: Wei, Ziyang, et al.
Published: (2025)

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling
by: Dexheimer, Niklas, et al.
Published: (2024)

Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
by: Wei, Ziyang, et al.
Published: (2023)

High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
by: Zhu, Wanrong, et al.
Published: (2024)

Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction
by: Wei, Ziyang, et al.
Published: (2026)

Training Diagonal Linear Networks with Stochastic Sharpness-Aware Minimization
by: Clara, Gabriel, et al.
Published: (2025)

Statistical Inference for Differentially Private Stochastic Gradient Descent
by: Xia, Xintao, et al.
Published: (2025)

Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent
by: Chang, Xiangyu, et al.
Published: (2022)

Sharp asymptotic theory for Q-learning with LDTZ learning rate and its generalization
by: Bonnerjee, Soham, et al.
Published: (2026)

High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares
by: Lin, Shurong, et al.
Published: (2025)

Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks
by: Rangriz, Parsa
Published: (2025)

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

Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training
by: Zhao, Shen-Yi, et al.
Published: (2020)

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

Central limit theorems for the outputs of fully convolutional neural networks with time series input
by: Betken, Annika, et al.
Published: (2026)

Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems
by: Zhong, Yanjie, et al.
Published: (2024)

Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees
by: Chen, Ziqin, et al.
Published: (2026)

Spike-timing-dependent Hebbian learning as noisy gradient descent
by: Dexheimer, Niklas, et al.
Published: (2025)

On the VC dimension of deep group convolutional neural networks
by: Sepliarskaia, Anna, et al.
Published: (2024)

Dropout Regularization Versus $\ell_2$-Penalization in the Linear Model
by: Clara, Gabriel, et al.
Published: (2023)

High-Dimensional Sparse Data Low-rank Representation via Accelerated Asynchronous Parallel Stochastic Gradient Descent
by: Hu, Qicong, et al.
Published: (2024)

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

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

Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
by: Chen, Hao, et al.
Published: (2021)

Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport
by: Hundrieser, Shayan, et al.
Published: (2026)

Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent
by: Wu, Tong
Published: (2025)

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

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

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

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
by: Wang, Puyu, et al.
Published: (2023)

Semi-Supervised Learning on Graphs using Graph Neural Networks
by: Chen, Juntong, et al.
Published: (2026)

Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
by: Birrell, Jeremiah, et al.
Published: (2024)

Trustworthiness of Stochastic Gradient Descent in Distributed Learning
by: Li, Hongyang, et al.
Published: (2024)

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

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

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

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

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

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