Saved in:
| Main Authors: | Cao, Dinghao, Guo, Zheng-Chu, Shi, Lei |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.07670 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024)
by: Chen, Haotian, et al.
Published: (2024)
Optimal Rates for Generalization of Gradient Descent for Deep ReLU Classification
by: Li, Yuanfan, et al.
Published: (2025)
by: Li, Yuanfan, et al.
Published: (2025)
Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
by: Ma, Wenquan, et al.
Published: (2026)
by: Ma, Wenquan, et al.
Published: (2026)
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
by: Shi, Lei, et al.
Published: (2024)
by: Shi, Lei, et al.
Published: (2024)
Truncated Kernel Stochastic Gradient Descent on Spheres
by: Bai, Jinhui, et al.
Published: (2024)
by: Bai, Jinhui, et al.
Published: (2024)
On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
by: Li, Bingrui, et al.
Published: (2024)
by: Li, Bingrui, 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)
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)
Stochastic Gradient Descent with Momentum is Algorithmically Stable
by: Lei, Yunwen, et al.
Published: (2026)
by: Lei, Yunwen, et al.
Published: (2026)
A Theoretical Analysis of Noise Geometry in Stochastic Gradient Descent
by: Wang, Mingze, et al.
Published: (2023)
by: Wang, Mingze, et al.
Published: (2023)
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 Adaptive Gradient Descent Without Descent
by: Aujol, Jean-François, et al.
Published: (2025)
by: Aujol, Jean-François, 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)
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)
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)
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
by: Zhang, Chenyang, et al.
Published: (2025)
by: Zhang, Chenyang, et al.
Published: (2025)
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 in the Saddle-to-Saddle Regime of Deep Linear Networks
by: Corlouer, Guillaume, et al.
Published: (2026)
by: Corlouer, Guillaume, et al.
Published: (2026)
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)
Adjacent Leader Decentralized Stochastic Gradient Descent
by: He, Haoze, et al.
Published: (2024)
by: He, Haoze, et al.
Published: (2024)
Stochastic Gradient Descent for Nonparametric Additive Regression
by: Chen, Xin, et al.
Published: (2024)
by: Chen, Xin, et al.
Published: (2024)
Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networks
by: Xu, Xianliang, et al.
Published: (2024)
by: Xu, Xianliang, 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)
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)
Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions
by: Bai, Jinhui, et al.
Published: (2025)
by: Bai, Jinhui, et al.
Published: (2025)
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)
Dichotomy of Feature Learning and Unlearning: Fast-Slow Analysis on Neural Networks with Stochastic Gradient Descent
by: Imai, Shota, et al.
Published: (2026)
by: Imai, Shota, et al.
Published: (2026)
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)
On the different regimes of Stochastic Gradient Descent
by: Sclocchi, Antonio, et al.
Published: (2023)
by: Sclocchi, Antonio, 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)
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 Understanding the Generalizability of Delayed Stochastic Gradient Descent
by: Deng, Xiaoge, et al.
Published: (2023)
by: Deng, Xiaoge, et al.
Published: (2023)
Learning Curves of Stochastic Gradient Descent in Kernel Regression
by: Zhang, Haihan, et al.
Published: (2025)
by: Zhang, Haihan, et al.
Published: (2025)
Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
by: Li, Jiaqi, et al.
Published: (2025)
by: Li, Jiaqi, et al.
Published: (2025)
Derivatives of Stochastic Gradient Descent in parametric optimization
by: Iutzeler, Franck, et al.
Published: (2024)
by: Iutzeler, Franck, et al.
Published: (2024)
Adaptive Heavy-Tailed Stochastic Gradient Descent
by: Gong, Bodu, et al.
Published: (2025)
by: Gong, Bodu, et al.
Published: (2025)
Streaming Krylov-Accelerated Stochastic Gradient Descent
by: Thomas, Stephen
Published: (2025)
by: Thomas, Stephen
Published: (2025)
Similar Items
-
Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024) -
Optimal Rates for Generalization of Gradient Descent for Deep ReLU Classification
by: Li, Yuanfan, et al.
Published: (2025) -
Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
by: Ma, Wenquan, et al.
Published: (2026) -
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
by: Shi, Lei, et al.
Published: (2024) -
Truncated Kernel Stochastic Gradient Descent on Spheres
by: Bai, Jinhui, et al.
Published: (2024)