Vector subdivision schemes and multiple wavelets. - Rong-Qing Jia; Sherman D. Riemenschneider; Ding-Xuan Zhou (Math. Comput., 1998)

System identification using wavelet neural networks. - Daniel W. C. Ho; Jinhua Xu; Ding-Xuan Zhou (ECC, 1999)

Smoothness of Multiple Refinable Functions and Multiple Wavelets. - Rong-Qing Jia; Sherman D. Riemenschneider; Ding-Xuan Zhou (SIAM J. Matrix Anal. Appl., 1999)

Convergence of Subdivision Schemes Associated with Nonnegative Masks. - Rong-Qing Jia; Ding-Xuan Zhou (SIAM J. Matrix Anal. Appl., 2000)

Binomial Matrices. - Geoff Boyd; Charles A. Micchelli; Gilbert Strang; Ding-Xuan Zhou (Adv. Comput. Math., 2001)

Self-Similar Lattice Tilings and Subdivision Schemes. - Ding-Xuan Zhou (SIAM J. Math. Anal., 2001)

Supports of Locally Linearly Independent M-Refinable Functions, Attractors of Iterated Function Systems and Tilings. - Hoi Ling Cheung; Canqin Tang; Ding-Xuan Zhou (Adv. Comput. Math., 2002)

The covering number in learning theory. - Ding-Xuan Zhou (J. Complex., 2002)

Interpolatory orthogonal multiwavelets and refinable functions. - Ding-Xuan Zhou (IEEE Trans. Signal Process., 2002)

Properties of locally linearly independent refinable function vectors. - Gerlind Plonka; Ding-Xuan Zhou (J. Approx. Theory, 2003)

Capacity of reproducing kernel spaces in learning theory. - Ding-Xuan Zhou (IEEE Trans. Inf. Theory, 2003)

Modeling Language Evolution. - Felipe Cucker; Steve Smale; Ding-Xuan Zhou (Found. Comput. Math., 2004)

Support Vector Machine Soft Margin Classifiers: Error Analysis. - Di-Rong Chen; Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (J. Mach. Learn. Res., 2004)

SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming. - Qiang Wu 003; Ding-Xuan Zhou (Neural Comput., 2005)

Approximation with polynomial kernels and SVM classifiers. - Ding-Xuan Zhou; Kurt Jetter (Adv. Comput. Math., 2006)

Learning Rates of Least-Square Regularized Regression. - Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (Found. Comput. Math., 2006)

Learning Coordinate Covariances via Gradients. - Sayan Mukherjee 001; Ding-Xuan Zhou (J. Mach. Learn. Res., 2006)

Online Regularized Classification Algorithms. - Yiming Ying; Ding-Xuan Zhou (IEEE Trans. Inf. Theory, 2006)

Multi-kernel regularized classifiers. - Qiang Wu 003; Yiming Ying; Ding-Xuan Zhou (J. Complex., 2007)

Learnability of Gaussians with Flexible Variances. - Yiming Ying; Ding-Xuan Zhou (J. Mach. Learn. Res., 2007)

Learning and approximation by Gaussians on Riemannian manifolds. - Gui-Bo Ye; Ding-Xuan Zhou (Adv. Comput. Math., 2008)

Learning with sample dependent hypothesis spaces. - Qiang Wu 003; Ding-Xuan Zhou (Comput. Math. Appl., 2008)

Parzen windows for multi-class classification. - Zhi-Wei Pan; Dao-Hong Xiang; Quan-Wu Xiao; Ding-Xuan Zhou (J. Complex., 2008)

High order Parzen windows and randomized sampling. - Xiang-Jun Zhou; Ding-Xuan Zhou (Adv. Comput. Math., 2009)

Gradient learning in a classification setting by gradient descent. - Jia Cai; Hongyan Wang; Ding-Xuan Zhou (J. Approx. Theory, 2009)

Online Learning with Samples Drawn from Non-identical Distributions. - Ting Hu 002; Ding-Xuan Zhou (J. Mach. Learn. Res., 2009)

Classification with Gaussians and Convex Loss. - Dao-Hong Xiang; Ding-Xuan Zhou (J. Mach. Learn. Res., 2009)

Moving least-square method in learning theory. - Hong-Yan Wang; Dao-Hong Xiang; Ding-Xuan Zhou (J. Approx. Theory, 2010)

Hermite learning with gradient data. - Lei Shi 010; Xin Guo 003; Ding-Xuan Zhou (J. Comput. Appl. Math., 2010)

Learning with varying insensitive loss. - Dao-Hong Xiang; Ting Hu 002; Ding-Xuan Zhou (Appl. Math. Lett., 2011)

Optimal learning rates for least squares regularized regression with unbounded sampling. - Cheng Wang; Ding-Xuan Zhou (J. Complex., 2011)

Normal estimation on manifolds by gradient learning. - Lei Shi 010; Ding-Xuan Zhou (Numer. Linear Algebra Appl., 2011)

Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression. - Dao-Hong Xiang; Ting Hu 002; Ding-Xuan Zhou (J. Appl. Math., 2012)

Concentration estimates for learning with unbounded sampling. - Zheng-Chu Guo; Ding-Xuan Zhou (Adv. Comput. Math., 2013)

An approximation theory approach to learning with ℓ1 regularization. - Hong-Yan Wang; Quan-Wu Xiao; Ding-Xuan Zhou (J. Approx. Theory, 2013)

Learning theory approach to minimum error entropy criterion. - Ting Hu 002; Jun Fan; Qiang Wu 003; Ding-Xuan Zhou (J. Mach. Learn. Res., 2013)

Consistency Analysis of an Empirical Minimum Error Entropy Algorithm. - Jun Fan; Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (CoRR, 2014)

Asymptotic Behaviour of Extinction Probability of Interacting Branching Collision Processes. - Anyue Chen; Junping Li; Yiqing Chen; Ding-Xuan Zhou (J. Appl. Probab., 2014)

Online Pairwise Learning Algorithms with Kernels. - Yiming Ying; Ding-Xuan Zhou (CoRR, 2015)

Unregularized Online Learning Algorithms with General Loss Functions. - Yiming Ying; Ding-Xuan Zhou (CoRR, 2015)

Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition. - Ming Yuan; Ding-Xuan Zhou (CoRR, 2015)

Learning theory of randomized Kaczmarz algorithm. - Junhong Lin; Ding-Xuan Zhou (J. Mach. Learn. Res., 2015)

Fast Convergence of Online Pairwise Learning Algorithms. - Martin Boissier 002; Siwei Lyu; Yiming Ying; Ding-Xuan Zhou (AISTATS, 2016)

On the robustness of regularized pairwise learning methods based on kernels. - Andreas Christmann; Ding-Xuan Zhou (J. Complex., 2016)

Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes. - Xin Guo 003; Jun Fan; Ding-Xuan Zhou (J. Mach. Learn. Res., 2016)

Iterative Regularization for Learning with Convex Loss Functions. - Junhong Lin; Lorenzo Rosasco; Ding-Xuan Zhou (J. Mach. Learn. Res., 2016)

Online Pairwise Learning Algorithms. - Yiming Ying; Ding-Xuan Zhou (Neural Comput., 2016)

Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression. - Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (IEEE Trans. Signal Process., 2016)

Online regularized learning with pairwise loss functions. - Zheng-Chu Guo; Yiming Ying; Ding-Xuan Zhou (Adv. Comput. Math., 2017)

Generalization Error Bounds for Extreme Multi-class Classification. - Yunwen Lei; Ürün Dogan; Ding-Xuan Zhou; Marius Kloft (CoRR, 2017)

Online pairwise learning algorithms with convex loss functions. - Junhong Lin; Yunwen Lei; Bo Zhang; Ding-Xuan Zhou (Inf. Sci., 2017)

Approximation on variable exponent spaces by linear integral operators. - Bing-Zheng Li; Bo-Lu He; Ding-Xuan Zhou (J. Approx. Theory, 2017)

Distributed Semi-supervised Learning with Kernel Ridge Regression. - Xiangyu Chang; Shaobo Lin; Ding-Xuan Zhou (J. Mach. Learn. Res., 2017)

Distributed Learning with Regularized Least Squares. - Shaobo Lin; Xin Guo 003; Ding-Xuan Zhou (J. Mach. Learn. Res., 2017)

Analysis of Online Composite Mirror Descent Algorithm. - Yunwen Lei; Ding-Xuan Zhou (Neural Comput., 2017)

Modified Fejér sequences and applications. - Junhong Lin; Lorenzo Rosasco; Silvia Villa; Ding-Xuan Zhou (Comput. Optim. Appl., 2018)

Convergence of Online Mirror Descent Algorithms. - Yunwen Lei; Ding-Xuan Zhou (CoRR, 2018)

Universality of Deep Convolutional Neural Networks. - Ding-Xuan Zhou (CoRR, 2018)

Construction of Neural Networks for Realization of Localized Deep Learning. - Charles K. Chui; Shao-Bo Lin; Ding-Xuan Zhou (Frontiers Appl. Math. Stat., 2018)

Total stability of kernel methods. - Andreas Christmann; Dao-Hong Xiang; Ding-Xuan Zhou (Neurocomputing, 2018)

Learning Theory of Randomized Sparse Kaczmarz Method. - Yunwen Lei; Ding-Xuan Zhou (SIAM J. Imaging Sci., 2018)

Online Learning Algorithms Can Converge Comparably Fast as Batch Learning. - Junhong Lin; Ding-Xuan Zhou (IEEE Trans. Neural Networks Learn. Syst., 2018)

Optimal Stochastic and Online Learning with Individual Iterates. - Yunwen Lei; Peng Yang 008; Ke Tang 001; Ding-Xuan Zhou (NeurIPS, 2019)

Deep Neural Networks for Rotation-Invariance Approximation and Learning. - Charles K. Chui; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2019)

Fast Polynomial Kernel Classification for Massive Data. - Jinshan Zeng; Minrun Wu; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2019)

Deep Net Tree Structure for Balance of Capacity and Approximation Ability. - Charles K. Chui; Shao-Bo Lin; Ding-Xuan Zhou (Frontiers Appl. Math. Stat., 2019)

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. - Shao-Bo Lin; Yunwen Lei; Ding-Xuan Zhou (J. Mach. Learn. Res., 2019)

Data-Dependent Generalization Bounds for Multi-Class Classification. - Yunwen Lei; Ürün Dogan; Ding-Xuan Zhou; Marius Kloft (IEEE Trans. Inf. Theory, 2019)

Optimal learning rates for distribution regression. - Zhiying Fang; Zheng-Chu Guo; Ding-Xuan Zhou (J. Complex., 2020)

Distributed Kernel Ridge Regression with Communications. - Shao-Bo Lin; Di Wang 008; Ding-Xuan Zhou (J. Mach. Learn. Res., 2020)

Theory of deep convolutional neural networks II: Spherical analysis. - Zhiying Fang; Han Feng; Shuo Huang 003; Ding-Xuan Zhou (Neural Networks, 2020)

Theory of deep convolutional neural networks: Downsampling. - Ding-Xuan Zhou (Neural Networks, 2020)

Towards Understanding the Spectral Bias of Deep Learning. - Yuan Cao 006; Zhiying Fang; Yue Wu; Ding-Xuan Zhou; Quanquan Gu (IJCAI, 2021)

Robust Kernel-based Distribution Regression. - Zhan Yu; Daniel W. C. Ho; Ding-Xuan Zhou (CoRR, 2021)

Radial Basis Function Approximation with Distributively Stored Data on Spheres. - Han Feng; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2021)

Kernel gradient descent algorithm for information theoretic learning. - Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (J. Approx. Theory, 2021)

On ADMM in Deep Learning: Convergence and Saturation-Avoidance. - Jinshan Zeng; Shao-Bo Lin; Yuan Yao 011; Ding-Xuan Zhou (J. Mach. Learn. Res., 2021)

Theory of deep convolutional neural networks III: Approximating radial functions. - Tong Mao; Zhongjie Shi; Ding-Xuan Zhou (Neural Networks, 2021)

Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere. - Shaobo Lin; Yuguang Wang 001; Ding-Xuan Zhou (SIAM J. Numer. Anal., 2021)

Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression. - Jinshan Zeng; Yudong Xie; Xianglong Yu; John Lee; Ding-Xuan Zhou (EMNLP (Findings), 2022)

Stability and Generalization for Markov Chain Stochastic Gradient Methods. - Puyu Wang; Yunwen Lei; Yiming Ying; Ding-Xuan Zhou (NeurIPS, 2022)

Approximation of functions from Korobov spaces by deep convolutional neural networks. - Tong Mao; Ding-Xuan Zhou (Adv. Comput. Math., 2022)

Attention Enables Zero Approximation Error. - Zhiying Fang; Yidong Ouyang; Ding-Xuan Zhou; Guang Cheng 003 (CoRR, 2022)

Approximation analysis of CNNs from feature extraction view. - Han Feng; Jianfei Li; Ding-Xuan Zhou (CoRR, 2022)

A new activation for neural networks and its approximation. - Jianfei Li; Han Feng; Ding-Xuan Zhou (CoRR, 2022)

Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints. - Jinshan Zeng; Wotao Yin; Ding-Xuan Zhou (J. Sci. Comput., 2022)

CNN models for readability of Chinese texts. - Han Feng; Sizai Hou; Le-Yin Wei; Ding-Xuan Zhou (Math. Found. Comput., 2022)

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. - Zhi Han; Siquan Yu; Shao-Bo Lin; Ding-Xuan Zhou (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Universal Consistency of Deep Convolutional Neural Networks. - Shao-Bo Lin; Kaidong Wang; Yao Wang 003; Ding-Xuan Zhou (IEEE Trans. Inf. Theory, 2022)

Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data. - Charles K. Chui; Shao-Bo Lin; Bo Zhang; Ding-Xuan Zhou (IEEE Trans. Neural Networks Learn. Syst., 2022)

Generalization Analysis for Contrastive Representation Learning. - Yunwen Lei; Tianbao Yang; Yiming Ying; Ding-Xuan Zhou (ICML, 2023)

Optimal rates of approximation by shallow ReLUk neural networks and applications to nonparametric regression. - Yunfei Yang; Ding-Xuan Zhou (CoRR, 2023)

Approximation of Nonlinear Functionals Using Deep ReLU Networks. - Linhao Song; Jun Fan; Di-Rong Chen; Ding-Xuan Zhou (CoRR, 2023)

Distributed Gradient Descent for Functional Learning. - Zhan Yu; Jun Fan; Ding-Xuan Zhou (CoRR, 2023)

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks. - Puyu Wang; Yunwen Lei; Di Wang 015; Yiming Ying; Ding-Xuan Zhou (CoRR, 2023)

Optimal Estimates for Pairwise Learning with Deep ReLU Networks. - Junyu Zhou; Shuo Huang 003; Han Feng; Ding-Xuan Zhou (CoRR, 2023)

Learning Theory of Distribution Regression with Neural Networks. - Zhongjie Shi; Zhan Yu; Ding-Xuan Zhou (CoRR, 2023)

Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning. - Zhi Han; Baichen Liu; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2023)

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks. - Guanhang Lei; Zhen Lei; Lei Shi 010; Chenyu Zeng; Ding-Xuan Zhou (CoRR, 2023)

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos. - Di Wang 008; Xiaotong Liu; Shao-Bo Lin; Ding-Xuan Zhou (CoRR, 2023)

Lifting the Veil: Unlocking the Power of Depth in Q-learning. - Shao-Bo Lin; Tao Li; Shaojie Tang 001; Yao Wang 003; Ding-Xuan Zhou (CoRR, 2023)

Federated learning for minimizing nonsmooth convex loss functions. - Le-Yin Wei; Zhan Yu; Ding-Xuan Zhou (Math. Found. Comput., 2023)

Generalization Analysis of Pairwise Learning for Ranking With Deep Neural Networks. - Shuo Huang 003; Junyu Zhou; Han Feng; Ding-Xuan Zhou (Neural Comput., 2023)

Deep learning theory of distribution regression with CNNs. - Zhan Yu; Ding-Xuan Zhou (Adv. Comput. Math., 2023)

Approximation of smooth functionals using deep ReLU networks. - Linhao Song; Ying Liu; Jun Fan; Ding-Xuan Zhou (Neural Networks, 2023)

Generalization Analysis of CNNs for Classification on Spheres. - Han Feng; Shuo Huang 003; Ding-Xuan Zhou (IEEE Trans. Neural Networks Learn. Syst., 2023)

Rates of approximation by ReLU shallow neural networks. - Tong Mao; Ding-Xuan Zhou (J. Complex., 2023)

Nonlinear functional regression by functional deep neural network with kernel embedding. - Zhongjie Shi; Jun Fan; Linhao Song; Ding-Xuan Zhou; Johan A. K. Suykens (CoRR, 2024)

On the rates of convergence for learning with convolutional neural networks. - Yunfei Yang; Han Feng; Ding-Xuan Zhou (CoRR, 2024)

Generalization analysis with deep ReLU networks for metric and similarity learning. - Junyu Zhou; Puyu Wang; Ding-Xuan Zhou (CoRR, 2024)

Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks. - Guangrui Yang; Jianfei Li; Ming Li 065; Han Feng; Ding-Xuan Zhou (CoRR, 2024)

Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks. - Jianfei Li; Han Feng; Ding-Xuan Zhou (CoRR, 2024)

Spherical Analysis of Learning Nonlinear Functionals. - Zhenyu Yang; Shuo Huang 003; Han Feng; Ding-Xuan Zhou (CoRR, 2024)

Differentially private stochastic gradient descent with low-noise. - Puyu Wang; Yunwen Lei; Yiming Ying; Ding-Xuan Zhou (Neurocomputing, 2024)

Approximation of functions from Korobov spaces by shallow neural networks. - Yuqing Liu; Tong Mao; Ding-Xuan Zhou (Inf. Sci., 2024)

Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks. - Yunfei Yang; Ding-Xuan Zhou (J. Mach. Learn. Res., 2024)

Classification with Deep Neural Networks and Logistic Loss. - Zihan Zhang; Lei Shi 010; Ding-Xuan Zhou (J. Mach. Learn. Res., 2024)

Sketching with Spherical Designs for Noisy Data Fitting on Spheres. - Shao-Bo Lin; Di Wang 008; Ding-Xuan Zhou (SIAM J. Sci. Comput., 2024)

SignReLU neural network and its approximation ability. - Jianfei Li; Han Feng; Ding-Xuan Zhou (J. Comput. Appl. Math., 2024)

Distributed Gradient Descent for Functional Learning. - Zhan Yu; Jun Fan; Zhongjie Shi; Ding-Xuan Zhou (IEEE Trans. Inf. Theory, 2024)

Deep Trajectory Recovery Approach of Offline Vehicles in the Internet of Vehicles. - Xiao Han 004; Ding-Xuan Zhou; Guojiang Shen; Xiangjie Kong 001; Yulong Zhao (IEEE Trans. Veh. Technol., 2024)

Analysis of regularized federated learning. - Langming Liu; Ding-Xuan Zhou (Neurocomputing, 2025)

Generalization Analysis of Transformers in Distribution Regression. - Peilin Liu; Ding-Xuan Zhou (Neural Comput., 2025)

Generalization Guarantees of Gradient Descent for Shallow Neural Networks. - Puyu Wang; Yunwen Lei; Di Wang 015; Yiming Ying; Ding-Xuan Zhou (Neural Comput., 2025)

Approximation of functionals on Korobov spaces with Fourier Functional Networks. - Peilin Liu; Yuqing Liu; Xiang Zhou 001; Ding-Xuan Zhou (Neural Networks, 2025)

Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach. - Qin Fang; Lei Shi; Min Xu; Ding-Xuan Zhou (CoRR, 2025)

Generalization Performance of Empirical Risk Minimization on Over-Parameterized Deep ReLU Nets. - Shao-Bo Lin; Yao Wang 003; Ding-Xuan Zhou (IEEE Trans. Inf. Theory, 2025)

Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition. - Nathanael Tepakbong; Ding-Xuan Zhou; Xiang Zhou (CoRR, 2025)

Optimal Convergence Rates of Deep Neural Network Classifiers. - Zihan Zhang; Lei Shi 010; Ding-Xuan Zhou (CoRR, 2025)