On the rate of convergence of an over-parametrized deep neural network regression estimate learned by gradient descent
Fuente:
arXiv
Saved in:
| Main Author: | Kohler, Michael |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the rate of convergence of an over-parametrized Transformer classifier learned by gradient descent
by: Kohler, Michael, et al.
Published: (2023)
by: Kohler, Michael, et al.
Published: (2023)
Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization
by: Kohler, Michael, et al.
Published: (2024)
by: Kohler, Michael, et al.
Published: (2024)
Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
by: Kohler, Michael, et al.
Published: (2024)
by: Kohler, Michael, et al.
Published: (2024)
On the rates of convergence for learning with convolutional neural networks
by: Yang, Yunfei, et al.
Published: (2024)
by: Yang, Yunfei, et al.
Published: (2024)
On the existence of the maximum likelihood estimate and convergence rate under gradient descent for multi-class logistic regression
by: Nwaigwe, Dwight, et al.
Published: (2020)
by: Nwaigwe, Dwight, et al.
Published: (2020)
Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model
by: Kohler, Michael, et al.
Published: (2020)
by: Kohler, Michael, et al.
Published: (2020)
Statistically guided deep learning
by: Kohler, Michael, et al.
Published: (2025)
by: Kohler, Michael, et al.
Published: (2025)
Local convergence rates of the nonparametric least squares estimator with applications to transfer learning
by: Schmidt-Hieber, Johannes, et al.
Published: (2022)
by: Schmidt-Hieber, Johannes, et al.
Published: (2022)
Spike-timing-dependent Hebbian learning as noisy gradient descent
by: Dexheimer, Niklas, et al.
Published: (2025)
by: Dexheimer, Niklas, et al.
Published: (2025)
Minimax Optimal rates of convergence in the shuffled regression, unlinked regression, and deconvolution under vanishing noise
by: Durot, Cecile, et al.
Published: (2024)
by: Durot, Cecile, et al.
Published: (2024)
Precise gradient descent training dynamics for finite-width multi-layer neural networks
by: Han, Qiyang, et al.
Published: (2025)
by: Han, Qiyang, et al.
Published: (2025)
One-step corrected projected stochastic gradient descent for statistical estimation
by: Brouste, Alexandre, et al.
Published: (2023)
by: Brouste, Alexandre, et al.
Published: (2023)
Nonparametric regression using over-parameterized shallow ReLU neural networks
by: Yang, Yunfei, et al.
Published: (2023)
by: Yang, Yunfei, et al.
Published: (2023)
Adversarial learning for nonparametric regression: Minimax rate and adaptive estimation
by: Peng, Jingfu, et al.
Published: (2025)
by: Peng, Jingfu, et al.
Published: (2025)
Nonparametric logistic regression with deep learning
by: Yara, Atsutomo, et al.
Published: (2024)
by: Yara, Atsutomo, et al.
Published: (2024)
Minimax rates of convergence for nonparametric regression under adversarial attacks
by: Peng, Jingfu, et al.
Published: (2024)
by: Peng, Jingfu, et al.
Published: (2024)
Treatment effect estimation under convergent network interference
by: Park, Bryan, et al.
Published: (2026)
by: Park, Bryan, et al.
Published: (2026)
Semiparametric M-estimation with overparameterized neural networks
by: Yan, Shunxing, et al.
Published: (2025)
by: Yan, Shunxing, et al.
Published: (2025)
Convergence guarantees for forward gradient descent in the linear regression model
by: Bos, Thijs, et al.
Published: (2023)
by: Bos, Thijs, et al.
Published: (2023)
Finite sample rates of convergence for the Bigraphical and Tensor graphical Lasso estimators
by: Zhou, Shuheng, et al.
Published: (2023)
by: Zhou, Shuheng, et al.
Published: (2023)
Constrained recursive kernel density/regression estimation by stochastic quasi-gradient methods
by: Norkin, Vladimir, et al.
Published: (2024)
by: Norkin, Vladimir, et al.
Published: (2024)
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
by: Stankewitz, Bernhard, et al.
Published: (2024)
by: Stankewitz, Bernhard, et al.
Published: (2024)
Rates of convergence for nonparametric estimation of singular distributions using generative adversarial networks
by: Lee, Jeyong, et al.
Published: (2022)
by: Lee, Jeyong, et al.
Published: (2022)
Rates of convergence for density estimation with generative adversarial networks
by: Puchkin, Nikita, et al.
Published: (2021)
by: Puchkin, Nikita, et al.
Published: (2021)
Causal inference through multi-stage learning and doubly robust deep neural networks
by: Zhang, Yuqian, et al.
Published: (2024)
by: Zhang, Yuqian, et al.
Published: (2024)
Gradient descent for deep equilibrium single-index models
by: Dandapanthula, Sanjit, et al.
Published: (2025)
by: Dandapanthula, Sanjit, et al.
Published: (2025)
Finding planted cliques using gradient descent
by: Gheissari, Reza, et al.
Published: (2023)
by: Gheissari, Reza, et al.
Published: (2023)
On the VC dimension of deep group convolutional neural networks
by: Sepliarskaia, Anna, et al.
Published: (2024)
by: Sepliarskaia, Anna, et al.
Published: (2024)
Long-time dynamics and universality of nonconvex gradient descent
by: Han, Qiyang
Published: (2025)
by: Han, Qiyang
Published: (2025)
Parametric convergence rate of a non-parametric estimator in multivariate mixtures of power series distributions under conditional independence
by: Balabdaoui, Fadoua, et al.
Published: (2025)
by: Balabdaoui, Fadoua, et al.
Published: (2025)
Penalized spline estimation of principal components for sparse functional data: rates of convergence
by: He, Shiyuan, et al.
Published: (2024)
by: He, Shiyuan, et al.
Published: (2024)
Minimax rates of convergence for the nonparametric estimation of the diffusion coefficient from time-homogeneous SDE paths
by: Mintsa, Eddy Michel Ella
Published: (2025)
by: Mintsa, Eddy Michel Ella
Published: (2025)
On estimation of skewed stable linear regression
by: Kawamo, Eitaro, et al.
Published: (2024)
by: Kawamo, Eitaro, et al.
Published: (2024)
Universality of high-dimensional scaling limits of stochastic gradient descent
by: Gheissari, Reza, et al.
Published: (2025)
by: Gheissari, Reza, et al.
Published: (2025)
Fast Spawn\&Prune (FS\&P): Global convergence of stochastic conic particle gradient descent via birth/death process
by: De Castro, Yohann, et al.
Published: (2026)
by: De Castro, Yohann, et al.
Published: (2026)
Learning single index model with gradient descent: spectral initialization and precise asymptotics
by: Chen, Yuchen, et al.
Published: (2025)
by: Chen, Yuchen, et al.
Published: (2025)
The Poisson tensor completion parametric estimator
by: Dunlavy, Daniel M., et al.
Published: (2025)
by: Dunlavy, Daniel M., et al.
Published: (2025)
Partial heteroscedastic deconvolution estimation in nonparametric regression
by: Thiam, Baba
Published: (2026)
by: Thiam, Baba
Published: (2026)
Posterior and variational inference for deep neural networks with heavy-tailed weights
by: Castillo, Ismaël, et al.
Published: (2024)
by: Castillo, Ismaël, et al.
Published: (2024)
How many samples are needed to train a deep neural network?
by: Golestaneh, Pegah, et al.
Published: (2024)
by: Golestaneh, Pegah, et al.
Published: (2024)
Similar Items
-
On the rate of convergence of an over-parametrized Transformer classifier learned by gradient descent
by: Kohler, Michael, et al.
Published: (2023) -
Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization
by: Kohler, Michael, et al.
Published: (2024) -
Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
by: Kohler, Michael, et al.
Published: (2024) -
On the rates of convergence for learning with convolutional neural networks
by: Yang, Yunfei, et al.
Published: (2024) -
On the existence of the maximum likelihood estimate and convergence rate under gradient descent for multi-class logistic regression
by: Nwaigwe, Dwight, et al.
Published: (2020)