Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Ma, Wenquan, Sui, Yang, Teng, Jiaye, Wang, Bohan, Xu, Jing, Yang, Jingqin |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
by: Xu, Huangyu, et al.
Published: (2026)
by: Xu, Huangyu, et al.
Published: (2026)
Stochastic Gradient Descent for Two-layer Neural Networks
by: Cao, Dinghao, et al.
Published: (2024)
by: Cao, Dinghao, et al.
Published: (2024)
Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024)
by: Chen, Haotian, et al.
Published: (2024)
Adjacent Leader Decentralized Stochastic Gradient Descent
by: He, Haoze, et al.
Published: (2024)
by: He, Haoze, et al.
Published: (2024)
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent with Predictable Virtual Noise
by: Partohaghighi, Mohammad
Published: (2026)
by: Partohaghighi, Mohammad
Published: (2026)
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)
Tight Generalization Error Bounds for Stochastic Gradient Descent in Non-convex Learning
by: Xiong, Wenjun, et al.
Published: (2025)
by: Xiong, Wenjun, et al.
Published: (2025)
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
by: Wang, Puyu, et al.
Published: (2023)
by: Wang, Puyu, et al.
Published: (2023)
Stochastic Gradient Descent with Adaptive Data
by: Che, Ethan, et al.
Published: (2024)
by: Che, Ethan, et al.
Published: (2024)
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)
Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression
by: Yang, Yingzhen, et al.
Published: (2024)
by: Yang, Yingzhen, et al.
Published: (2024)
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
by: Shi, Lei, et al.
Published: (2024)
by: Shi, Lei, 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)
Limit Theorems for Stochastic Gradient Descent with Infinite Variance
by: Blanchet, Jose, et al.
Published: (2024)
by: Blanchet, Jose, et al.
Published: (2024)
Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov-Arnold Networks
by: Wang, Puyu, et al.
Published: (2026)
by: Wang, Puyu, et al.
Published: (2026)
A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimization
by: Zhu, Yuchen, et al.
Published: (2024)
by: Zhu, Yuchen, et al.
Published: (2024)
Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
by: Xu, Conglong, et al.
Published: (2025)
by: Xu, Conglong, et al.
Published: (2025)
Stochastic Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)
by: Aujol, Jean-François, et al.
Published: (2025)
Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks
by: Tsilivis, Nikolaos, et al.
Published: (2024)
by: Tsilivis, Nikolaos, et al.
Published: (2024)
PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
by: Xu, Mingjing, et al.
Published: (2024)
by: Xu, Mingjing, et al.
Published: (2024)
Stochastic Gradient Descent in the Saddle-to-Saddle Regime of Deep Linear Networks
by: Corlouer, Guillaume, et al.
Published: (2026)
by: Corlouer, Guillaume, et al.
Published: (2026)
Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study
by: Alexander, Yotam, et al.
Published: (2025)
by: Alexander, Yotam, et al.
Published: (2025)
Stochastic Gradient Descent with Momentum is Algorithmically Stable
by: Lei, Yunwen, et al.
Published: (2026)
by: Lei, Yunwen, et al.
Published: (2026)
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)
Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)
by: Xu, Xianliang, et al.
Published: (2024)
A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent
by: Wang, Mingze, et al.
Published: (2023)
by: Wang, Mingze, et al.
Published: (2023)
Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent
by: Lam, Henry, et al.
Published: (2023)
by: Lam, Henry, et al.
Published: (2023)
On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks
by: Gao, Yihang, et al.
Published: (2024)
by: Gao, Yihang, et al.
Published: (2024)
Stochastic Gradient Descent with Strategic Querying
by: Jiang, Nanfei, et al.
Published: (2025)
by: Jiang, Nanfei, et al.
Published: (2025)
Sharp Generalization for Nonparametric Regression in Interpolation Space by Over-Parameterized Neural Networks Trained with Preconditioned Gradient Descent and Early Stopping
by: Yang, Yingzhen, et al.
Published: (2024)
by: Yang, Yingzhen, et al.
Published: (2024)
Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent
by: Hsiao, Yen-Che, et al.
Published: (2024)
by: Hsiao, Yen-Che, et al.
Published: (2024)
Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels
by: Yang, Jia-Qi, et al.
Published: (2025)
by: Yang, Jia-Qi, et al.
Published: (2025)
Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients
by: Krahn, Maximilian, et al.
Published: (2023)
by: Krahn, Maximilian, et al.
Published: (2023)
On the Theory of Continual Learning with Gradient Descent for Neural Networks
by: Taheri, Hossein, et al.
Published: (2025)
by: Taheri, Hossein, et al.
Published: (2025)
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 Gaussian Processes Done Right
by: Lin, Jihao Andreas, et al.
Published: (2023)
by: Lin, Jihao Andreas, et al.
Published: (2023)
Towards Learning Stochastic Population Models by Gradient Descent
by: Kreikemeyer, Justin N., et al.
Published: (2024)
by: Kreikemeyer, Justin N., et al.
Published: (2024)
Similar Items
-
Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
by: Xu, Huangyu, et al.
Published: (2026) -
Stochastic Gradient Descent for Two-layer Neural Networks
by: Cao, Dinghao, et al.
Published: (2024) -
Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024) -
Adjacent Leader Decentralized Stochastic Gradient Descent
by: He, Haoze, et al.
Published: (2024) -
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent with Predictable Virtual Noise
by: Partohaghighi, Mohammad
Published: (2026)