Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction
Fuente:
arXiv
Saved in:
| Main Authors: | Wei, Ziyang, Zhu, Wanrong, Lyu, Jingyang, Wu, Wei Biao |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
by: Wei, Ziyang, et al.
Published: (2023)
by: Wei, Ziyang, et al.
Published: (2023)
Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
by: Wei, Ziyang, et al.
Published: (2025)
by: Wei, Ziyang, et al.
Published: (2025)
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
by: Zhu, Wanrong, et al.
Published: (2024)
by: Zhu, Wanrong, et al.
Published: (2024)
Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
by: Li, Jiaqi, et al.
Published: (2025)
by: Li, Jiaqi, et al.
Published: (2025)
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
by: Du, Xinchen, et al.
Published: (2025)
by: Du, Xinchen, et al.
Published: (2025)
Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
by: Li, Jiaqi, et al.
Published: (2024)
by: Li, Jiaqi, et al.
Published: (2024)
Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
by: Akhtiamov, Danil, et al.
Published: (2026)
by: Akhtiamov, Danil, et al.
Published: (2026)
Estimation of Toeplitz Covariance Matrices using Overparameterized Gradient Descent
by: Busbib, Daniel, et al.
Published: (2025)
by: Busbib, Daniel, et al.
Published: (2025)
The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient
by: Li, Jichu, et al.
Published: (2026)
by: Li, Jichu, et al.
Published: (2026)
Stochastic Normalized Gradient Descent with Momentum for Large-Batch Training
by: Zhao, Shen-Yi, et al.
Published: (2020)
by: Zhao, Shen-Yi, et al.
Published: (2020)
Covariant Gradient Descent
by: Guskov, Dmitry, et al.
Published: (2025)
by: Guskov, Dmitry, et al.
Published: (2025)
Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks
by: Peleg, Amit, et al.
Published: (2024)
by: Peleg, Amit, et al.
Published: (2024)
Random Matrix Theory for Stochastic Gradient Descent
by: Park, Chanju, et al.
Published: (2024)
by: Park, Chanju, et al.
Published: (2024)
A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond
by: Li, Yipeng, et al.
Published: (2025)
by: Li, Yipeng, et al.
Published: (2025)
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)
by: Li, Tianyou, et al.
Published: (2023)
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks
by: Li, Binghui, et al.
Published: (2024)
by: Li, Binghui, et al.
Published: (2024)
Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)
by: Aujol, Jean-François, et al.
Published: (2025)
On the Generalization of Stochastic Gradient Descent with Momentum
by: Ramezani-Kebrya, Ali, et al.
Published: (2018)
by: Ramezani-Kebrya, Ali, et al.
Published: (2018)
A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent
by: Wang, Mingze, et al.
Published: (2023)
by: Wang, Mingze, et al.
Published: (2023)
The Implicit Bias of Gradient Descent on Separable Data
by: Soudry, Daniel, et al.
Published: (2017)
by: Soudry, Daniel, et al.
Published: (2017)
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
by: Shen, Wei, et al.
Published: (2023)
by: Shen, Wei, et al.
Published: (2023)
Adjacent Leader Decentralized Stochastic Gradient Descent
by: He, Haoze, et al.
Published: (2024)
by: He, Haoze, et al.
Published: (2024)
A Bootstrap Perspective on Stochastic Gradient Descent
by: Lan, Hongjian, et al.
Published: (2025)
by: Lan, Hongjian, et al.
Published: (2025)
Stochastic Gradient Descent for Nonparametric Additive Regression
by: Chen, Xin, et al.
Published: (2024)
by: Chen, Xin, et al.
Published: (2024)
Bolstering Stochastic Gradient Descent with Model Building
by: Birbil, S. Ilker, et al.
Published: (2021)
by: Birbil, S. Ilker, et al.
Published: (2021)
Descend or Rewind? Stochastic Gradient Descent Unlearning
by: Mu, Siqiao, et al.
Published: (2025)
by: Mu, Siqiao, et al.
Published: (2025)
The Implicit Bias of Gradient Descent on Separable Multiclass Data
by: Ravi, Hrithik, et al.
Published: (2024)
by: Ravi, Hrithik, et al.
Published: (2024)
Training Instabilities Induce Flatness Bias in Gradient Descent
by: Wang, Lawrence, et al.
Published: (2025)
by: Wang, Lawrence, et al.
Published: (2025)
Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent
by: Ziyin, Liu, et al.
Published: (2024)
by: Ziyin, Liu, et al.
Published: (2024)
Online Covariance Matrix Estimation in Sketched Newton Methods
by: Kuang, Wei, et al.
Published: (2025)
by: Kuang, Wei, et al.
Published: (2025)
Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)
by: Che, Ethan, et al.
Published: (2024)
Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)
by: Jiang, Nanfei, et al.
Published: (2025)
Analysis of Estimating the Bayes Rule for Gaussian Mixture Models with a Specified Missing-Data Mechanism
by: Lyu, Ziyang
Published: (2022)
by: Lyu, Ziyang
Published: (2022)
Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
by: Yu, Siyuan, et al.
Published: (2024)
by: Yu, Siyuan, et al.
Published: (2024)
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks
by: Cai, Yuhang, et al.
Published: (2025)
by: Cai, Yuhang, et al.
Published: (2025)
Convergence of Alternating Gradient Descent for Matrix Factorization
by: Ward, Rachel, et al.
Published: (2023)
by: Ward, Rachel, et al.
Published: (2023)
Elastic Multi-Gradient Descent for Parallel Continual Learning
by: Lyu, Fan, et al.
Published: (2024)
by: Lyu, Fan, et al.
Published: (2024)
Personalized Federated Learning with Exact Stochastic Gradient Descent
by: Nikoloutsopoulos, Sotirios, et al.
Published: (2022)
by: Nikoloutsopoulos, Sotirios, et al.
Published: (2022)
Stochastic Gradient Descent for Two-layer Neural Networks
by: Cao, Dinghao, et al.
Published: (2024)
by: Cao, Dinghao, et al.
Published: (2024)
Stochastic Gradient Descent for Gaussian Processes Done Right
by: Lin, Jihao Andreas, et al.
Published: (2023)
by: Lin, Jihao Andreas, et al.
Published: (2023)
Similar Items
-
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
by: Wei, Ziyang, et al.
Published: (2023) -
Central Limit Theorems for Stochastic Gradient Descent Quantile Estimators
by: Wei, Ziyang, et al.
Published: (2025) -
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
by: Zhu, Wanrong, et al.
Published: (2024) -
Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
by: Li, Jiaqi, et al.
Published: (2025) -
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
by: Du, Xinchen, et al.
Published: (2025)