Cover Image

Plug-In Classification of Drift Functions in Diffusion Processes Using Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Yuzhen, Fan, Jiarong, Liu, Yating
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Drift Estimation for Diffusion Processes Using Neural Networks Based on Discretely Observed Independent Paths
by: Zhao, Yuzhen, et al.
Published: (2025)

Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration
by: Di Gregorio, Simone, et al.
Published: (2025)

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

Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots
by: Guan, Vincent, et al.
Published: (2024)

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

Kernel Debiased Plug-in Estimation based on the Universal Least Favorable Submodel
by: Chen, Haiyi, et al.
Published: (2026)

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

Exploring the Complexity of Deep Neural Networks through Functional Equivalence
by: Shen, Guohao
Published: (2023)

Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation
by: Wang, Xiaohan, et al.
Published: (2025)

Asymptotic Classification Error for Heavy-Tailed Renewal Processes
by: Rong, Xinhui, et al.
Published: (2024)

Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes
by: Bergna, Richard, et al.
Published: (2025)

Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories
by: Zhang, Haobo, et al.
Published: (2024)

Conformal Prediction Under Generalized Covariate Shift with Posterior Drift
by: Wang, Baozhen, et al.
Published: (2025)

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

Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
by: Hauth, Jeremiah, et al.
Published: (2024)

Generalization Analysis for Classification on Korobov Space
by: Liu, Yuqing
Published: (2025)

Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks
by: Liu, Xingchi, et al.
Published: (2024)

Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data
by: Fu, Hengyu, et al.
Published: (2024)

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

Stress-Aware Learning under KL Drift via Trust-Decayed Mirror Descent
by: Raj, Gabriel Nixon
Published: (2025)

Simultaneous Approximation of the Score Function and Its Derivatives by Deep Neural Networks
by: Yakovlev, Konstantin, et al.
Published: (2025)

Variational Optimality of Föllmer Processes in Generative Diffusions
by: Chen, Yifan, et al.
Published: (2026)

An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
by: Chen, Minshuo, et al.
Published: (2024)

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)

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

Non-identifiability distinguishes Neural Networks among Parametric Models
by: Chatterjee, Sourav, et al.
Published: (2025)

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

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

Adaptation to Intrinsic Dependence in Diffusion Language Models
by: Zhao, Yunxiao, et al.
Published: (2026)

Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compression
by: Shi, Haoming, et al.
Published: (2026)

Upper Bounds for Local Learning Coefficients of Three-Layer Neural Networks
by: Kurumadani, Yuki
Published: (2026)

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

Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
by: Zhao, Yuxuan, et al.
Published: (2026)

Provable Generalization Bounds for Deep Neural Networks with Momentum-Adaptive Gradient Dropout
by: Safder, Adeel
Published: (2025)

Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks
by: Bobbia, Benjamin, et al.
Published: (2024)

Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees
by: Fan, Jianqing, et al.
Published: (2025)

Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
by: Kungurtsev, Vyacheslav, et al.
Published: (2024)

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
by: Frei, Spencer, et al.
Published: (2022)

Learning under Latent Group Sparsity via Diffusion on Networks
by: Ghosh, Subhroshekhar, et al.
Published: (2025)