Ding-Xuan Zhou

发表

ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY

S. Smale, Ding-Xuan Zhou, 2003 .

Shannon sampling and function reconstruction from point values

S. Smale, Ding-Xuan Zhou, 2004 .

Density Problem and Approximation Error in Learning Theory

Ding-Xuan Zhou, 2013 .

ONLINE LEARNING WITH MARKOV SAMPLING

S. Smale, Ding-Xuan Zhou, 2009 .

Learning Theory Estimates via Integral Operators and Their Approximations

S. Smale, Ding-Xuan Zhou, 2007 .

Shannon sampling II: Connections to learning theory

S. Smale, Ding-Xuan Zhou, 2005 .

Learning gradients on manifolds

Ding-Xuan Zhou, Sayan Mukherjee, Qiang Wu, 2010, 1002.4283.

The covering number in learning theory

Ding-Xuan Zhou, Ding-Xuan Zhou, 2002, J. Complex..

Derivative reproducing properties for kernel methods in learning theory

Ding-Xuan Zhou, 2008 .

Solvability of linear systems of PDE’s with constant coefficients

Ding-Xuan Zhou, 1999 .

Geometry on Probability Spaces

S. Smale, Ding-Xuan Zhou, 2009 .

LEARNING BY NONSYMMETRIC KERNELS WITH DATA DEPENDENT SPACES AND

Ding-Xuan Zhou, Quan-Wu Xiao, 2010 .

An approximation theory approach to learning with ℓ1 regularization

Ding-Xuan Zhou, Quan-Wu Xiao, Hong-Yan Wang, 2013, J. Approx. Theory.

Concentration estimates for learning with ℓ1-regularizer and data dependent hypothesis spaces

Ding-Xuan Zhou, Lei Shi, Yunlong Feng, 2011 .

Learning Theory: An Approximation Theory Viewpoint

Ding-Xuan Zhou, F. Cucker, 2007 .

Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data

Charles K. Chui, Bo Zhang, Ding-Xuan Zhou, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Optimal Learning Rates for Kernel Partial Least Squares

Ding-Xuan Zhou, Shaobo Lin, 2018 .

Learning gradients by a gradient descent algorithm

Ding-Xuan Zhou, Xue-Mei Dong, 2008 .

Capacity of reproducing kernel spaces in learning theory

Ding-Xuan Zhou, Ding-Xuan Zhou, 2003, IEEE Transactions on Information Theory.

Reproducing Kernel Hilbert Spaces Associated with Analytic Translation-Invariant Mercer Kernels

Ding-Xuan Zhou, Hongkai Sun, 2008 .

Learning Gradients and Feature Selection on Manifolds

Ding-Xuan Zhou, Sayan Mukherjee, Qiang Wu, .

Modeling Language Evolution

Felipe Cucker, Stephen Smale, Ding-Xuan Zhou, 2004, Found. Comput. Math..

Learning theory of distributed spectral algorithms

Ding-Xuan Zhou, Shaobo Lin, Zheng-Chu Guo, 2017 .

Learning Theory: An Approximation Theory Viewpoint: On the bias–variance problem

Felipe Cucker, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007 .

Deep distributed convolutional neural networks: Universality

Ding-Xuan Zhou, 2018, Analysis and Applications.

Learning rates for the risk of kernel-based quantile regression estimators in additive models

Ding-Xuan Zhou, A. Christmann, 2014, 1405.3379.

Order of linear approximation from shift-invariant spaces

Ding-Xuan Zhou, K. Jetter, 1995 .

Approximation by Multiple Refinable Functions

Ding-Xuan Zhou, S. Riemenschneider, R. Jia, 1997, Canadian Journal of Mathematics.

Existence of multiple refinable distributions.

Ding-Xuan Zhou, 1997 .

The $p$-norm joint spectral radius for even integers

Ding-Xuan Zhou, 1998 .

Multiple Refinable Hermite Interpolants

Ding-Xuan Zhou, 2000 .

Local linear independence of refinable vectors of functions

Ding-Xuan Zhou, R. Jia, T. Goodman, 2000, Proceedings of the Royal Society of Edinburgh: Section A Mathematics.

Lp solutions of refinement equations

Ding-Xuan Zhou, R. Jia, K. Lau, 2001 .

Compactly supported wavelet bases for Sobolev spaces

Ding-Xuan Zhou, R. Jia, Jianzhong Wang, 2003 .

Online Classification with Varying Gaussians

Ding-Xuan Zhou, Ting Hu, 2010 .

Fully online classification by regularization

Ding-Xuan Zhou, Gui-Bo Ye, 2007 .

Fully Online Classiflcation by Regularization y

Ding-Xuan Zhou, Gui-Bo Ye, 2006 .

Learning Theory: From Regression to Classification

Ding-Xuan Zhou, Qiang Wu, Yiming Ying, 2006 .

Online learning for quantile regression and support vector regression

Ding-Xuan Zhou, Ting Hu, Daohong Xiang, 2012 .

Learning Theory Approach to a System Identification Problem Involving Atomic Norm

Ding-Xuan Zhou, Luoqing Li, 2015 .

Error bounds for learning the kernel

Ding-Xuan Zhou, C. Micchelli, M. Pontil, 2016 .

Regularization schemes for minimum error entropy principle

Ding-Xuan Zhou, Qiang Wu, Jun Fan, 2015 .

SVM LEARNING AND Lp APPROXIMATION BY GAUSSIANS ON RIEMANNIAN MANIFOLDS

Ding-Xuan Zhou, Gui-Bo Ye, 2009 .

The Covering Number in LearningTheory 1

Ding-Xuan Zhou, 2002 .

Thresholded spectral algorithms for sparse approximations

Dao-Hong Xiang, Ding-Xuan Zhou, Zheng-Chu Guo, 2017 .

System identification using wavelet neural networks

Ding-Xuan Zhou, D. Ho, Jinhua Xu, 1999, 1999 European Control Conference (ECC).

Modelling Language Evolution ∗†

Felipe Cucker, Ding-Xuan Zhou, S. Smale, 2003 .

SOME CHARACTERIZATIONS FOR BOX SPLINE WAVELETS AND LINEAR DIOPHANTINE EQUATIONS

Ding-Xuan Zhou, 1998 .

Box Splines with Rational Directions and Linear Diophantine Equations

Ding-Xuan Zhou, 1996 .

Mean size of wavelet packets

Ding-Xuan Zhou, M. Nielsen, 2002 .

Extinction Probability of Interacting Branching Collision Processes

Ding-Xuan Zhou, Junping Li, Anyue Chen, 2012, Advances in Applied Probability.

On wavelets inL1

Ding-Xuan Zhou, 1994 .

Two-Scale Homogeneous Functions in Wavelet Analysis

Ding-Xuan Zhou, 2002 .

Spectra of subdivision operators

Ding-Xuan Zhou, 2000 .

Convergence of online mirror descent

Ding-Xuan Zhou, Yunwen Lei, 2018, Applied and Computational Harmonic Analysis.

LEARNING BY NONSYMMETRIC KERNELS WITH DATA DEPENDENT SPACES AND

Ding-Xuan Zhou, Quan-Wu Xiao, 2010 .

On a paper of Mazhar and Totik

Ding-Xuan Zhou, 1993 .

Characterisation of correctness of cardinal interpolation with shifted three-directional box splines

Ding-Xuan Zhou, Kurt Jetterf, 1995, Proceedings of the Royal Society of Edinburgh: Section A Mathematics.

Differentially Private Stochastic Gradient Descent with Low-Noise

Ding-Xuan Zhou, Yiming Ying, Puyu Wang, 2022, 2209.04188.

Stability and Generalization for Markov Chain Stochastic Gradient Methods

Ding-Xuan Zhou, Yiming Ying, Puyu Wang, 2022, NeurIPS.

Computational Harmonic Analysis Organizing Committee Conference Secretary General Information Lecture Theatres Foreign Currency Exchange Email/fax Sightseeing Trip Non-uniform Sampling and Reconstruction in Shift-invariant Spaces with Applications to Signal and Image Processing Multiwavelet Transfor

Say Song Goh, Ding-Xuan Zhou, W. Dahmen, 2022 .

A new activation for neural networks and its approximation

Ding-Xuan Zhou, Jianfei Li, Han Feng, 2022, ArXiv.

Approximation Analysis of CNNs from a Feature Extraction View

Ding-Xuan Zhou, Jianfei Li, Han Feng, 2022, SSRN Electronic Journal.

Learning Theory Approach to a System Identification Problem Involving Atomic Norm

Ding-Xuan Zhou, Luoqing Li, 2015, Journal of Fourier Analysis and Applications.

Modified Fejér sequences and applications

Ding-Xuan Zhou, L. Rosasco, Junhong Lin, 2017, Computational optimization and applications.

Optimal Learning Rates for Kernel Partial Least Squares

Ding-Xuan Zhou, Shao-Bo Lin, 2017, Journal of Fourier Analysis and Applications.

Fast Polynomial Kernel Classification for Massive Data

Ding-Xuan Zhou, Shao-Bo Lin, Jinshan Zeng, 2019, ArXiv.

Approximation of functions from Korobov spaces by deep convolutional neural networks

Ding-Xuan Zhou, Tong Mao, 2022, Advances in Computational Mathematics.

Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints

Ding-Xuan Zhou, W. Yin, Jinshan Zeng, 2022, Journal of Scientific Computing.

Radial Basis Function Approximation with Distributively Stored Data on Spheres

Ding-Xuan Zhou, Shao-Bo Lin, Han Feng, 2021, Constructive Approximation.

Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics)

Felipe Cucker, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007 .

Learning Theory: An Approximation Theory Viewpoint: Index

Felipe Cucker, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007 .

Concentration estimates for learning with unbounded sampling

Ding-Xuan Zhou, Zheng-Chu Guo, 2011, Advances in Computational Mathematics.

Distributed Kernel-Based Gradient Descent Algorithms

Ding-Xuan Zhou, Shao-Bo Lin, 2017, Constructive Approximation.

Frames and Other Bases in Abstract and Function Spaces

Ding-Xuan Zhou, H. Mhaskar, I. Pesenson, 2017 .

Learning Theory: An Approximation Theory Viewpoint: References

Felipe Cucker, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007 .

Approximating functions with multi-features by deep convolutional neural networks

Ding-Xuan Zhou, Tong Mao, Zhongjie Shi, 2022, Analysis and Applications.

Theory of deep convolutional neural networks: Downsampling

Ding-Xuan Zhou, Ding-Xuan Zhou, 2020, Neural Networks.

Universality of Deep Convolutional Neural Networks pdf

Ding-Xuan Zhou, Ding-Xuan Zhou, 2018, Applied and Computational Harmonic Analysis.

Multi-kernel regularized classifiers

Yiming Ying, Ding-Xuan Zhou, Qiang Wu, 2007, J. Complex..

Support Vector Machine Soft Margin Classifiers: Error Analysis

Yiming Ying, Ding-Xuan Zhou, Qiang Wu, 2004, J. Mach. Learn. Res..

Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition pdf

Ming Yuan, Ding-Xuan Zhou, M. Yuan, 2015, ArXiv.

Data-Dependent Generalization Bounds for Multi-Class Classification

Marius Kloft, Yunwen Lei, Ding-Xuan Zhou, 2017, IEEE Transactions on Information Theory.

Generalization Error Bounds for Extreme Multi-class Classification pdf

Marius Kloft, Yunwen Lei, Ürün Dogan, 2017, ArXiv.

Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes

Jun Fan, Ding-Xuan Zhou, Xin Guo, 2016, J. Mach. Learn. Res..

On the robustness of regularized pairwise learning methods based on kernels

Andreas Christmann, Ding-Xuan Zhou, Ding-Xuan Zhou, 2015, J. Complex..

Online regularized learning with pairwise loss functions

Yiming Ying, Ding-Xuan Zhou, Zheng-Chu Guo, 2016, Advances in Computational Mathematics.

Normal estimation on manifolds by gradient learning

Lei Shi, Ding-Xuan Zhou, Ding-Xuan Zhou, 2011, Numer. Linear Algebra Appl..

Cardinal interpolation with shifted three-directional box splines

Ding-Xuan Zhou, K. Jetter, P. Binev, 1992, Proceedings of the Royal Society of Edinburgh: Section A Mathematics.

Parzen windows for multi-class classification

Dao-Hong Xiang, Ding-Xuan Zhou, Quan-Wu Xiao, 2008, J. Complex..

Approximation on variable exponent spaces by linear integral operators

Ding-Xuan Zhou, Bing-Zheng Li, Bo-Lu He, 2017, J. Approx. Theory.

Learnability of Gaussians with Flexible Variances

Yiming Ying, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007, J. Mach. Learn. Res..

High order Parzen windows and randomized sampling

Ding-Xuan Zhou, Xiang-Jun Zhou, Ding-Xuan Zhou, 2009, Adv. Comput. Math..

Characterization Theorems for the Approximation by a Family of Operators

Ding-Xuan Zhou, D. H. Mache, 1996 .

Local smoothness of functions and Bernstein-Durrmeyer operators

Ding-Xuan Zhou, H. Gonska, 1995 .

Best direct and converse results for Lagrange-type operators

Ding-Xuan Zhou, D. H. Mache, 1995 .

On grouping effect of elastic net

Ding-Xuan Zhou, Ding-Xuan Zhou, 2013 .

Towards Understanding the Spectral Bias of Deep Learning pdf

Yuan Cao, Quanquan Gu, Ding-Xuan Zhou, 2019, IJCAI.

Construction of Neural Networks for Realization of Localized Deep Learning pdf

Charles K. Chui, Ding-Xuan Zhou, Shaobo Lin, 2018, Front. Appl. Math. Stat..

Construction of Real-Valued Wavelets by Symmetry

Ding-Xuan Zhou, Ding-Xuan Zhou, 1995 .

Mathematisches Forschungsinstitut Oberwolfach Learning Theory and Approximation

Ding-Xuan Zhou, Hong Kong, 2008 .

Smoothness of Multiple Refinable Functions and Multiple Wavelets

Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou, 1999, SIAM J. Matrix Anal. Appl..

Vector subdivision schemes and multiple wavelets

Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou, 1998, Math. Comput..

Converse theorems for multidimensional Kantorovich operators

Ding-Xuan Zhou, Ding-Xuan Zhou, 1993 .

Inverse theorems for multidimensional Bernstein-Durrmeyer operators in L p

Ding-Xuan Zhou, 1992 .

Universal Consistency of Deep Convolutional Neural Networks pdf

Shao-Bo Lin, Kaidong Wang, Ding-Xuan Zhou, 2021, IEEE Transactions on Information Theory.

Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets

Ding-Xuan Zhou, Shao-Bo Lin, Yao Wang, 2021, ArXiv.

Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints

Wotao Yin, Jinshan Zeng, Ding-Xuan Zhou, 2021, Journal of Scientific Computing.

Modified Fejér sequences and applications

Lorenzo Rosasco, Silvia Villa, Ding-Xuan Zhou, 2015, Comput. Optim. Appl..

Learning Coordinate Covariances via Gradients

Sayan Mukherjee, Ding-Xuan Zhou, Ding-Xuan Zhou, 2006, J. Mach. Learn. Res..

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization

Zhi Han, Shao-Bo Lin, Ding-Xuan Zhou, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Deep Neural Networks for Rotation-Invariance Approximation and Learning pdf

Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou, 2019, Analysis and Applications.

Approximation with polynomial kernels and SVM classifiers

Kurt Jetter, Ding-Xuan Zhou, Ding-Xuan Zhou, 2006, Adv. Comput. Math..

Learning Rates of Least-Square Regularized Regression

Yiming Ying, Ding-Xuan Zhou, Qiang Wu, 2006, Found. Comput. Math..

Binomial Matrices

Charles A. Micchelli, Gilbert Strang, Ding-Xuan Zhou, 2001, Adv. Comput. Math..

Optimal learning rates for least squares regularized regression with unbounded sampling

Cheng Wang, Ding-Xuan Zhou, Ding-Xuan Zhou, 2011, J. Complex..

Online Learning Algorithms Can Converge Comparably Fast as Batch Learning

Ding-Xuan Zhou, Junhong Lin, Ding-Xuan Zhou, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression

Ting Hu, Ding-Xuan Zhou, Qiang Wu, 2016, IEEE Transactions on Signal Processing.

Stability of refinable functions, multiresolution analysis, and Haar bases

Ding-Xuan Zhou, Ding-Xuan Zhou, 1996 .

Learning Theory of Randomized Sparse Kaczmarz Method

Yunwen Lei, Ding-Xuan Zhou, Ding-Xuan Zhou, 2018, SIAM J. Imaging Sci..

Learning theory of randomized Kaczmarz algorithm

Ding-Xuan Zhou, Junhong Lin, Ding-Xuan Zhou, 2015, J. Mach. Learn. Res..

Sketching with Spherical Designs for Noisy Data Fitting on Spheres

Ding-Xuan Zhou, Shao-Bo Lin, Di Wang, 2023, ArXiv.

Deep learning theory of distribution regression with CNNs

Ding-Xuan Zhou, Zhan Yu, 2023, Advances in Computational Mathematics.

Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions

Ding-Xuan Zhou, Tong Mao, Zhongjie Shi, 2021, Neural Networks.

Theory of Deep Convolutional Neural Networks II: Spherical Analysis

Shuo Huang, Ding-Xuan Zhou, Han Feng, 2020, Neural Networks.

Nonparametric regression using over-parameterized shallow ReLU neural networks

Ding-Xuan Zhou, Yunfei Yang, 2023, ArXiv.

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping

Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou, 2019, J. Mach. Learn. Res..

Unregularized Online Learning Algorithms with General Loss Functions pdf

Yiming Ying, Ding-Xuan Zhou, Ding-Xuan Zhou, 2015, ArXiv.

Classification with Gaussians and Convex Loss

Dao-Hong Xiang, Ding-Xuan Zhou, Ding-Xuan Zhou, 2009, J. Mach. Learn. Res..

Online Regularized Classification Algorithms

Yiming Ying, Ding-Xuan Zhou, Ding-Xuan Zhou, 2006, IEEE Transactions on Information Theory.

Optimal rates of approximation by shallow ReLUk neural networks and applications to nonparametric regression

Ding-Xuan Zhou, Yunfei Yang, 2023, Constructive Approximation.

Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression

Ting Hu, Dao-Hong Xiang, Ding-Xuan Zhou, 2012, J. Appl. Math..

Learning with varying insensitive loss

Dao-Hong Xiang, Ding-Xuan Zhou, Ting Hu, 2011, Appl. Math. Lett..

Online pairwise learning algorithms with convex loss functions

Bo Zhang, Yunwen Lei, Ding-Xuan Zhou, 2017, Inf. Sci..

Online regularized learning with pairwise loss functions

Ding-Xuan Zhou, Yiming Ying, Zheng-Chu Guo, 2017, Adv. Comput. Math..

Online Pairwise Learning Algorithms

Yiming Ying, Ding-Xuan Zhou, Ding-Xuan Zhou, 2016, Neural Computation.

Iterative Regularization for Learning with Convex Loss Functions

Lorenzo Rosasco, Ding-Xuan Zhou, Junhong Lin, 2015, J. Mach. Learn. Res..

Learning theory approach to minimum error entropy criterion

Jun Fan, Ting Hu, Ding-Xuan Zhou, 2012, J. Mach. Learn. Res..

Hermite learning with gradient data

Lei Shi, Ding-Xuan Zhou, Xin Guo, 2010, J. Comput. Appl. Math..

Moving least-square method in learning theory

Dao-Hong Xiang, Ding-Xuan Zhou, Hong-Yan Wang, 2010, J. Approx. Theory.

Learning and approximation by Gaussians on Riemannian manifolds

Ding-Xuan Zhou, Gui-Bo Ye, Ding-Xuan Zhou, 2009, Adv. Comput. Math..

SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming

Ding-Xuan Zhou, Qiang Wu, Ding-Xuan Zhou, 2005, Neural Computation.

Learning with sample dependent hypothesis spaces

Ding-Xuan Zhou, Qiang Wu, Ding-Xuan Zhou, 2008, Comput. Math. Appl..

Consistency Analysis of an Empirical Minimum Error Entropy Algorithm pdf

Jun Fan, Ting Hu, Ding-Xuan Zhou, 2014, ArXiv.

Analysis of Singular Value Thresholding Algorithm for Matrix Completion

Ding-Xuan Zhou, Ding-Xuan Zhou, Yunwen Lei, 2019, Journal of Fourier Analysis and Applications.

Analysis of Online Composite Mirror Descent Algorithm

Yunwen Lei, Ding-Xuan Zhou, Ding-Xuan Zhou, 2017, Neural Computation.

Distributed Learning with Regularized Least Squares pdf

Ding-Xuan Zhou, Shaobo Lin, Xin Guo, 2016, J. Mach. Learn. Res..

Distributed Gradient Descent for Functional Learning

Ding-Xuan Zhou, Zhan Yu, Jun Fan, 2023, arXiv.org.

Rates of Convergence of Randomized Kaczmarz Algorithms in Hilbert Spaces

Ding-Xuan Zhou, Junhong Lin, Xin Guo, 2022, Applied and Computational Harmonic Analysis.

Robust kernel-based distribution regression

Ding-Xuan Zhou, Daniel W C Ho, Zhongjie Shi, 2021, Inverse Problems.

Distributed kernel gradient descent algorithm for minimum error entropy principle

Ting Hu, Ding-Xuan Zhou, Qiang Wu, 2020 .

Distributed filtered hyperinterpolation for noisy data on the sphere pdf

Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou, 2019, SIAM J. Numer. Anal..

Distributed Kernel-Based Gradient Descent Algorithms

Ding-Xuan Zhou, Shaobo Lin, Ding-Xuan Zhou, 2018 .

Distributed Semi-supervised Learning with Kernel Ridge Regression

Xiangyu Chang, Ding-Xuan Zhou, Shaobo Lin, 2017, J. Mach. Learn. Res..

Optimal Stochastic and Online Learning with Individual Iterates

Ke Tang, Ding-Xuan Zhou, Yunwen Lei, 2019, NeurIPS.

Distributed Kernel Ridge Regression with Communications

Di Wang, Shao-Bo Lin, Ding-Xuan Zhou, 2020, J. Mach. Learn. Res..

Learning Theory: An Approximation Theory Viewpoint: Index

Ding-Xuan Zhou, F. Cucker, 2007 .

Optimal Estimates for Pairwise Learning with Deep ReLU Networks

Ding-Xuan Zhou, Han Feng, Shuo Huang, 2023, ArXiv.

Generalization Analysis for Contrastive Representation Learning

Ding-Xuan Zhou, Tianbao Yang, Yiming Ying, 2023, ICML.

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks

Ding-Xuan Zhou, Zhen Lei, Lei Shi, 2023, ArXiv.

Generalization Analysis of Pairwise Learning for Ranking With Deep Neural Networks

Ding-Xuan Zhou, Han Feng, Shuo Huang, 2023, Neural Computation.

Generalization Analysis of CNNs for Classification on Spheres

Ding-Xuan Zhou, Han Feng, Shuo Huang, 2021, IEEE Transactions on Neural Networks and Learning Systems.

Learning Theory of Distribution Regression with Neural Networks

Ding-Xuan Zhou, Zhan Yu, Zhongjie Shi, 2023, ArXiv.

SignReLU neural network and its approximation ability

Ding-Xuan Zhou, Jianfei Li, Han Feng, 2022, J. Comput. Appl. Math..

Total stability of kernel methods

Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou, 2017, Neurocomputing.

Rates of Approximation by ReLU Shallow Neural Networks

Ding-Xuan Zhou, Tong Mao, 2023, J. Complex..

CNN models for readability of Chinese texts

Ding-Xuan Zhou, Sizai Hou, Han Feng, 2022, Math. Found. Comput..

Learning Theory: An Approximation Theory Viewpoint: The framework of learning

Ding-Xuan Zhou, F. Cucker, 2007 .

Deep learning theory of distribution regression with CNNs

Ding-Xuan Zhou, Zhan Yu, 2023, Advances in Computational Mathematics.

Ding-Xuan Zhou, Ding-Xuan Zhou, 2001, SIAM J. Math. Anal..

Concentration estimates for learning with unbounded sampling

Ding-Xuan Zhou, Zheng-Chu Guo, Ding-Xuan Zhou, 2013, Adv. Comput. Math..

Approximation of smooth functionals using deep ReLU networks

Ding-Xuan Zhou, Jun Fan, Linhao Song, 2023, Neural Networks.

Approximation of Nonlinear Functionals Using Deep ReLU Networks

Ding-Xuan Zhou, Dirong Chen, Jun Fan, 2023, Journal of Fourier Analysis and Applications.

Properties of locally linearly independent refinable function vectors

Gerlind Plonka-Hoch, Ding-Xuan Zhou, Ding-Xuan Zhou, 2003, J. Approx. Theory.

Attention Enables Zero Approximation Error

Ding-Xuan Zhou, Guang Cheng, Yidong Ouyang, 2022, ArXiv.

On Smoothness Characterized by Bernstein Type Operators

Ding-Xuan Zhou, Ding-Xuan Zhou, 1995 .

On a conjecture of Z. Ditzian

Ding-Xuan Zhou, 1992 .

L p -inverse theorems for beta operators

Ding-Xuan Zhou, 1991 .

Modeling Language Evolution

S. Smale, Ding-Xuan Zhou, F. Cucker, 2004, Found. Comput. Math..

Analysis of Approximation by Linear Operators on Variable Spaces and Applications in Learning Theory

Ding-Xuan Zhou, Bing-Zheng Li, Ding-Xuan Zhou, 2014 .

Functions with Infinite Masks

Ding-Xuan Zhou, G. Strang, 2007 .

Compactly Supported Refinable Functions with Infinite Masks

Ding-Xuan Zhou, G. Strang, 1999 .

Iterative Regularization for Learning with Convex Loss Functions

Ding-Xuan Zhou, L. Rosasco, Junhong Lin, 2015, J. Mach. Learn. Res..

On ADMM in Deep Learning: Convergence and Saturation-Avoidance

Ding-Xuan Zhou, Yuan Yao, Shaobo Lin, 2019, J. Mach. Learn. Res..