Cover Image

Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Haobo, Lai, Jianfa, Li, Yicheng, Lin, Qian, Liu, Jun S.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the Optimality of Misspecified Spectral Algorithms
by: Zhang, Haobo, et al.
Published: (2023)

On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory
by: Chen, Guhan, et al.
Published: (2024)

Neural Tangent Kernel of Neural Networks with Loss Informed by Differential Operators
by: Gan, Weiye, et al.
Published: (2025)

Optimal Rate of Kernel Regression in Large Dimensions
by: Lu, Weihao, et al.
Published: (2023)

The Optimality of Kernel Classifiers in Sobolev Space
by: Lai, Jianfa, et al.
Published: (2024)

The phase diagram of kernel interpolation in large dimensions
by: Zhang, Haobo, et al.
Published: (2024)

Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets
by: Yu, Zixiong, et al.
Published: (2024)

Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks
by: Taheri, Mahsa, et al.
Published: (2022)

An Empirical Analysis of the Laplace and Neural Tangent Kernels
by: Lencevičius, Ronaldas Paulius
Published: (2022)

Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators
by: Nguyen, Mike, et al.
Published: (2026)

Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay
by: Li, Yicheng, et al.
Published: (2024)

Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned Kernels
by: Huang, Dongming, et al.
Published: (2025)

A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics
by: Lin, Licong, et al.
Published: (2025)

Optimal Confidence Band for Kernel Gradient Flow Estimator
by: Cheng, Yuqian, et al.
Published: (2026)

General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions
by: Li, Yicheng
Published: (2026)

Memorization With Neural Nets: Going Beyond the Worst Case
by: Dirksen, Sjoerd, et al.
Published: (2023)

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
by: Shen, Zhaiming, et al.
Published: (2025)

The Statistical Accuracy of Neural Posterior and Likelihood Estimation
by: Frazier, David T., et al.
Published: (2024)

Functional Bias and Tangent-Space Geometry in Variational Inference
by: Plummer, Sean
Published: (2026)

A Theory of Feature Learning in Kernel Models
by: Chen, Yunlu, et al.
Published: (2023)

Inference for Deep Neural Network Estimators in Generalized Nonparametric Models
by: Meng, Xuran, et al.
Published: (2025)

A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics
by: Ni, Yijin, et al.
Published: (2024)

A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI
by: Oko, Kazusato, et al.
Published: (2025)

Statistical Guarantees for Link Prediction using Graph Neural Networks
by: Chung, Alan, et al.
Published: (2024)

Approximation of RKHS Functionals by Neural Networks
by: Zhou, Tian-Yi, et al.
Published: (2024)

Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks
by: Zhao, Yuzhen, et al.
Published: (2026)

Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction
by: Tang, Yin, et al.
Published: (2024)

Scalable Kernel-Based Distances for Statistical Inference and Integration
by: Naslidnyk, Masha
Published: (2026)

Dense ReLU Neural Networks for Temporal-spatial Model
by: Padilla, Carlos Misael Madrid, et al.
Published: (2024)

Optimal Classification-based Anomaly Detection with Neural Networks: Theory and Practice
by: Zhou, Tian-Yi, et al.
Published: (2024)

Statistical Learning Theory for Distributional Classification
by: Fiedler, Christian
Published: (2026)

Consistency for Large Neural Networks: Regression and Classification
by: Zhan, Haoran, et al.
Published: (2024)

Phase Transitions for Feature Learning in Neural Networks
by: Montanari, Andrea, et al.
Published: (2026)

On the Saturation Effect of Kernel Ridge Regression
by: Li, Yicheng, et al.
Published: (2024)

Minimax and Adaptive Covariance Matrix Estimation under Differential Privacy
by: Cai, T. Tony, et al.
Published: (2026)

Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
by: Choi, Ikjun, et al.
Published: (2024)

Pointwise Generalization in Deep Neural Networks
by: Li, Shaojie, et al.
Published: (2026)

Affine Invariance in Continuous-Domain Convolutional Neural Networks
by: Mohaddes, Ali, et al.
Published: (2023)

Can Bayesian Neural Networks Make Confident Predictions?
by: Fisher, Katharine, et al.
Published: (2025)

Tensor Product Neural Networks for Functional ANOVA Model
by: Park, Seokhun, et al.
Published: (2025)