Jun Fan

Generalization analysis of deep CNNs under maximum correntropy criterion

Neural Networks

Jan 2024

journal-article

Distributed Gradient Descent for Functional Learning

IEEE Transactions on Information Theory

Jan 2024

journal-article

High-probability generalization bounds for pointwise uniformly stable algorithms

Applied and Computational Harmonic Analysis

Jan 2024

journal-article

Learning Korobov Functions by Correntropy and Convolutional Neural Networks

Neural Computation

Jan 2024

journal-article

Nonlinear functional regression by functional deep neural network with kernel embedding

arXiv

Jan 2024

other

On the convergence of gradient descent for robust functional linear regression

Journal of Complexity

Jan 2024

journal-article

Optimal prediction for kernel-based semi-functional linear regression

Analysis and Applications

Jan 2024

journal-article

SPECTRAL ALGORITHMS FOR FUNCTIONAL LINEAR REGRESSION

Communications on Pure and Applied Analysis

Jan 2024

journal-article

Approximation of Nonlinear Functionals Using Deep ReLU Networks

Journal of Fourier Analysis and Applications

Jan 2023

journal-article

Approximation of Nonlinear Functionals Using Deep ReLU Networks

arXiv

Jan 2023

other

Approximation of smooth functionals using deep ReLU networks

Neural Networks

Jan 2023

journal-article

Correction: Approximation of Nonlinear Functionals Using Deep ReLU Networks (Journal of Fourier Analysis and Applications, (2023), 29, 4, (50), 10.1007/s00041-023-10027-1)

Journal of Fourier Analysis and Applications

Jan 2023

journal-article

Distributed Gradient Descent for Functional Learning

arXiv

Jan 2023

other

Online gradient descent algorithms for functional data learning

Journal of Complexity

Jan 2022

journal-article

Comparison theorems on large-margin learning

International Journal of Wavelets, Multiresolution and Information Processing

Jan 2021

journal-article

Optimal learning with Gaussians and correntropy loss

Analysis and Applications

Jan 2021

journal-article

Optimal prediction for kernel-based semi-functional linear regression

arXiv

Jan 2021

other

A Statistical Learning Approach to Modal Regression

Journal of Machine Learning Research

Jan 2020

journal-article

Quantitative convergence analysis of kernel based large-margin unified machines

Communications on Pure and Applied Analysis

Jan 2020

journal-article

Convergence analysis of distributed multi-penalty regularized pairwise learning

Analysis and Applications

Jan 2020

journal-article

Convergence analysis of distributed multi-penalty regularized pairwise learning

Analysis and Applications

Jan 2020

journal-article

A statistical learning approach to modal regression

Journal of Machine Learning Research

Jan 2020

journal-article

Doubly robust estimation of average treatment effect revisited

arXiv

Jan 2020

other

Comparison theorems on large-margin learning

arXiv

Jan 2019

other

Optimal learning with Gaussians and correntropy loss

Analysis and Applications

Jan 2019

journal-article

An RKHS approach to estimate individualized treatment rules based on functional predictors

Mathematical Foundations of Computing

Jan 2019

journal-article

AN RKHS APPROACH TO ESTIMATE INDIVIDUALIZED TREATMENT RULES BASED ON FUNCTIONAL PREDICTORS

Mathematical Foundations of Computing

Jan 2019

journal-article

Quantifying predictive capability of electronic health records for the most harmful breast cancer.

Proceedings of SPIE--the International Society for Optical Engineering

Jan 2018

journal-article

Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

Jan 2018

journal-article

Quantifying predictive capability of electronic health records for the most harmful breast cancer

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Jan 2018

conference-paper

Breast Cancer Risk Prediction Using Electronic Health Records

2017 IEEE International Conference on Healthcare Informatics (ICHI)

Jan 2017

conference-paper

Breast Cancer Risk Prediction Using Electronic Health Records

Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017

Jan 2017

conference-paper

Learning rates for regularized least squares ranking algorithm

Analysis and Applications

Jan 2017

journal-article

Learning rates for regularized least squares ranking algorithm

Analysis and Applications

Jan 2017

journal-article

A statistical learning approach to modal regression

arXiv

Jan 2017

other

Breast Cancer Risk Prediction Using Electronic Health Records

2017 IEEE International Conference on Healthcare Informatics (ICHI)

Jan 2017

conference-paper

Sparsity and error analysis of empirical feature-based regularization schemes

Journal of Machine Learning Research

Jan 2016

journal-article

Structure-Leveraged Methods in Breast Cancer Risk Prediction

Journal of Machine Learning Research

Jan 2016

journal-article

Structure-leveraged methods in breast cancer risk prediction

Journal of Machine Learning Research

Jan 2016

journal-article

Comment

Journal of the American Statistical Association

Jan 2016

journal-article

Personalized Dose Finding Using Outcome Weighted Learning Comment

Journal of the American Statistical Association

Jan 2016

journal-article

Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

Academic Radiology

Jan 2016

journal-article

Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

Academic Radiology

Jan 2016

journal-article

Consistency analysis of an empirical minimum error entropy algorithm

Applied and Computational Harmonic Analysis

Jan 2016

conference-paper

Consistency analysis of an empirical minimum error entropy algorithm

Applied and Computational Harmonic Analysis

Jan 2016

journal-article

Discriminatory power of common genetic variants in personalized breast cancer diagnosis

Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment

Jan 2016

conference-paper

Discriminatory power of common genetic variants in personalized breast cancer diagnosis

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Jan 2016

conference-paper

Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes

Journal of Machine Learning Research

Jan 2016

journal-article

Parameterized BLOSUM matrices for protein alignment

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Jan 2015

journal-article

Parameterized BLOSUM Matrices for Protein Alignment

Ieee-Acm Transactions on Computational Biology and Bioinformatics

Jan 2015

journal-article

Regularization schemes for minimum error entropy principle

Analysis and Applications

Jan 2015

journal-article

Regularization schemes for minimum error entropy principle

Analysis and Applications

Jan 2015

journal-article

Learning Theory Approach to Minimum Error Entropy Criterion

Journal of Machine Learning Research

Jan 2013

journal-article

Learning theory approach to minimum error entropy criterion

Journal of Machine Learning Research

Jan 2013

journal-article

Jun Fan

Work

Hong Kong Baptist University

Hong Kong Baptist University

University of Wisconsin–Madison

Education

City University of Hong Kong

Beijing Normal University

Publications

Generalization analysis of deep CNNs under maximum correntropy criterion

Distributed Gradient Descent for Functional Learning

High-probability generalization bounds for pointwise uniformly stable algorithms

Learning Korobov Functions by Correntropy and Convolutional Neural Networks

Nonlinear functional regression by functional deep neural network with kernel embedding

On the convergence of gradient descent for robust functional linear regression

Optimal prediction for kernel-based semi-functional linear regression

SPECTRAL ALGORITHMS FOR FUNCTIONAL LINEAR REGRESSION

Approximation of Nonlinear Functionals Using Deep ReLU Networks

Approximation of Nonlinear Functionals Using Deep ReLU Networks

Approximation of smooth functionals using deep ReLU networks

Correction: Approximation of Nonlinear Functionals Using Deep ReLU Networks (Journal of Fourier Analysis and Applications, (2023), 29, 4, (50), 10.1007/s00041-023-10027-1)

Distributed Gradient Descent for Functional Learning

Online gradient descent algorithms for functional data learning

Comparison theorems on large-margin learning

Optimal learning with Gaussians and correntropy loss

Optimal prediction for kernel-based semi-functional linear regression

A Statistical Learning Approach to Modal Regression

Quantitative convergence analysis of kernel based large-margin unified machines

Convergence analysis of distributed multi-penalty regularized pairwise learning

Convergence analysis of distributed multi-penalty regularized pairwise learning

A statistical learning approach to modal regression

Doubly robust estimation of average treatment effect revisited

Comparison theorems on large-margin learning

Optimal learning with Gaussians and correntropy loss

An RKHS approach to estimate individualized treatment rules based on functional predictors

AN RKHS APPROACH TO ESTIMATE INDIVIDUALIZED TREATMENT RULES BASED ON FUNCTIONAL PREDICTORS

Quantifying predictive capability of electronic health records for the most harmful breast cancer.

Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age.

Quantifying predictive capability of electronic health records for the most harmful breast cancer

Breast Cancer Risk Prediction Using Electronic Health Records

Breast Cancer Risk Prediction Using Electronic Health Records

Learning rates for regularized least squares ranking algorithm

Learning rates for regularized least squares ranking algorithm

A statistical learning approach to modal regression

Breast Cancer Risk Prediction Using Electronic Health Records

Sparsity and error analysis of empirical feature-based regularization schemes

Structure-Leveraged Methods in Breast Cancer Risk Prediction

Structure-leveraged methods in breast cancer risk prediction

Comment

Personalized Dose Finding Using Outcome Weighted Learning Comment

Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

Consistency analysis of an empirical minimum error entropy algorithm

Consistency analysis of an empirical minimum error entropy algorithm

Discriminatory power of common genetic variants in personalized breast cancer diagnosis

Discriminatory power of common genetic variants in personalized breast cancer diagnosis

Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes

Parameterized BLOSUM matrices for protein alignment

Parameterized BLOSUM Matrices for Protein Alignment

Regularization schemes for minimum error entropy principle

Regularization schemes for minimum error entropy principle

Learning Theory Approach to Minimum Error Entropy Criterion

Learning theory approach to minimum error entropy criterion