Ding-Xuan Zhou

156 papers A* 4A 1C 1Journal 148Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Optimization and Generalization of Gradient Descent for Shallow ReLU Networks with Minimal Width. J. Mach. Learn. Res. Yunwen Lei, Puyu Wang, Yiming Ying, Ding-Xuan Zhou
2026	J	`jnl`	Two-Stage Data Synthesization: A Statistics-Driven Restricted Trade-off between Privacy and Prediction. CoRR Xiaotong Liu, Shao-Bo Lin, Jun Fan, Ding-Xuan Zhou
2025	J	`jnl`	Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos. J. Mach. Learn. Res. Shao-Bo Lin, Xiaotong Liu, Di Wang, Hai Zhang, Ding-Xuan Zhou
2025	J	`jnl`	Analysis of regularized federated learning. Neurocomputing Langming Liu, Ding-Xuan Zhou
2025	J	`jnl`	Approximation of functionals on Korobov spaces with Fourier Functional Networks. Neural Networks Peilin Liu, Yuqing Liu, Xiang Zhou, Ding-Xuan Zhou
2025	J	`jnl`	Generalization Analysis of Transformers in Distribution Regression. Neural Comput. Peilin Liu, Ding-Xuan Zhou
2025	J	`jnl`	Generalization Guarantees of Gradient Descent for Shallow Neural Networks. Neural Comput. Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou
2025	J	`jnl`	Generalization Performance of Empirical Risk Minimization on Over-Parameterized Deep ReLU Nets. IEEE Trans. Inf. Theory Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou
2025	J	`jnl`	Nonlinear functional regression by functional deep neural network with kernel embedding. J. Mach. Learn. Res. Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A. K. Suykens
2025	J	`jnl`	On the Rates of Convergence for Learning with Convolutional Neural Networks. SIAM J. Math. Data Sci. Yunfei Yang, Han Feng, Ding-Xuan Zhou
2025	J	`jnl`	Optimal Convergence Rates of Deep Neural Network Classifiers. CoRR Zihan Zhang, Lei Shi, Ding-Xuan Zhou
2025	J	`jnl`	Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition. CoRR Nathanael Tepakbong, Ding-Xuan Zhou, Xiang Zhou
2025	J	`jnl`	Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach. CoRR Qin Fang, Lei Shi, Min Xu, Ding-Xuan Zhou
2024	J	`jnl`	Analysis of regularized federated learning. CoRR Langming Liu, Ding-Xuan Zhou
2024	J	`jnl`	Approximation of functions from Korobov spaces by shallow neural networks. Inf. Sci. Yuqing Liu, Tong Mao, Ding-Xuan Zhou
2024	J	`jnl`	Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks. CoRR Guangrui Yang, Jianfei Li, Ming Li, Han Feng, Ding-Xuan Zhou
2024	J	`jnl`	Classification with Deep Neural Networks and Logistic Loss. J. Mach. Learn. Res. Zihan Zhang, Lei Shi, Ding-Xuan Zhou
2024	J	`jnl`	Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks. CoRR Jianfei Li, Han Feng, Ding-Xuan Zhou
2024	J	`jnl`	Deep Trajectory Recovery Approach of Offline Vehicles in the Internet of Vehicles. IEEE Trans. Veh. Technol. Xiao Han, Ding-Xuan Zhou, Guojiang Shen, Xiangjie Kong, Yulong Zhao
2024	J	`jnl`	Differentially private stochastic gradient descent with low-noise. Neurocomputing Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou
2024	J	`jnl`	Distributed Gradient Descent for Functional Learning. IEEE Trans. Inf. Theory Zhan Yu, Jun Fan, Zhongjie Shi, Ding-Xuan Zhou
2024	J	`jnl`	Generalization analysis with deep ReLU networks for metric and similarity learning. CoRR Junyu Zhou, Puyu Wang, Ding-Xuan Zhou
2024	J	`jnl`	Nonlinear functional regression by functional deep neural network with kernel embedding. CoRR Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A. K. Suykens
2024	J	`jnl`	Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks. J. Mach. Learn. Res. Yunfei Yang, Ding-Xuan Zhou
2024	J	`jnl`	On the rates of convergence for learning with convolutional neural networks. CoRR Yunfei Yang, Han Feng, Ding-Xuan Zhou
2024	J	`jnl`	SignReLU neural network and its approximation ability. J. Comput. Appl. Math. Jianfei Li, Han Feng, Ding-Xuan Zhou
2024	J	`jnl`	Sketching with Spherical Designs for Noisy Data Fitting on Spheres. SIAM J. Sci. Comput. Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
2024	J	`jnl`	Spherical Analysis of Learning Nonlinear Functionals. CoRR Zhenyu Yang, Shuo Huang, Han Feng, Ding-Xuan Zhou
2023	J	`jnl`	Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos. CoRR Di Wang, Xiaotong Liu, Shao-Bo Lin, Ding-Xuan Zhou
2023	J	`jnl`	Approximation of Nonlinear Functionals Using Deep ReLU Networks. CoRR Linhao Song, Jun Fan, Di-Rong Chen, Ding-Xuan Zhou
2023	J	`jnl`	Approximation of smooth functionals using deep ReLU networks. Neural Networks Linhao Song, Ying Liu, Jun Fan, Ding-Xuan Zhou
2023	J	`jnl`	Classification with Deep Neural Networks and Logistic Loss. CoRR Zihan Zhang, Lei Shi, Ding-Xuan Zhou
2023	J	`jnl`	Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning. CoRR Zhi Han, Baichen Liu, Shao-Bo Lin, Ding-Xuan Zhou
2023	J	`jnl`	Deep learning theory of distribution regression with CNNs. Adv. Comput. Math. Zhan Yu, Ding-Xuan Zhou
2023	J	`jnl`	Distributed Gradient Descent for Functional Learning. CoRR Zhan Yu, Jun Fan, Ding-Xuan Zhou
2023	J	`jnl`	Federated learning for minimizing nonsmooth convex loss functions. Math. Found. Comput. Le-Yin Wei, Zhan Yu, Ding-Xuan Zhou
2023	A*	`conf`	Generalization Analysis for Contrastive Representation Learning. ICML Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
2023	J	`jnl`	Generalization Analysis for Contrastive Representation Learning. CoRR Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
2023	J	`jnl`	Generalization Analysis of CNNs for Classification on Spheres. IEEE Trans. Neural Networks Learn. Syst. Han Feng, Shuo Huang, Ding-Xuan Zhou
2023	J	`jnl`	Generalization Analysis of Pairwise Learning for Ranking With Deep Neural Networks. Neural Comput. Shuo Huang, Junyu Zhou, Han Feng, Ding-Xuan Zhou
2023	J	`jnl`	Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks. CoRR Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou
2023	J	`jnl`	Learning Theory of Distribution Regression with Neural Networks. CoRR Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou
2023	J	`jnl`	Lifting the Veil: Unlocking the Power of Depth in Q-learning. CoRR Shao-Bo Lin, Tao Li, Shaojie Tang, Yao Wang, Ding-Xuan Zhou
2023	J	`jnl`	Nonparametric regression using over-parameterized shallow ReLU neural networks. CoRR Yunfei Yang, Ding-Xuan Zhou
2023	J	`jnl`	Optimal Estimates for Pairwise Learning with Deep ReLU Networks. CoRR Junyu Zhou, Shuo Huang, Han Feng, Ding-Xuan Zhou
2023	J	`jnl`	Optimal rates of approximation by shallow ReLU CoRR Yunfei Yang, Ding-Xuan Zhou
2023	J	`jnl`	Rates of Approximation by ReLU Shallow Neural Networks. CoRR Tong Mao, Ding-Xuan Zhou
2023	J	`jnl`	Rates of approximation by ReLU shallow neural networks. J. Complex. Tong Mao, Ding-Xuan Zhou
2023	J	`jnl`	Sketching with Spherical Designs for Noisy Data Fitting on Spheres. CoRR Shaobo Lin, Di Wang, Ding-Xuan Zhou
2023	J	`jnl`	Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks. CoRR Guanhang Lei, Zhen Lei, Lei Shi, Chenyu Zeng, Ding-Xuan Zhou
2022	C	`conf`	A distinctive collexeme analysis of near-synonym constructions "ying-dang/ying-gai + verb". PACLIC Zhuo Zhang, Meichun Liu, Ding-Xuan Zhou
2022	J	`jnl`	A new activation for neural networks and its approximation. CoRR Jianfei Li, Han Feng, Ding-Xuan Zhou
2022	J	`jnl`	Approximation analysis of CNNs from feature extraction view. CoRR Han Feng, Jianfei Li, Ding-Xuan Zhou
2022	J	`jnl`	Approximation of functions from Korobov spaces by deep convolutional neural networks. Adv. Comput. Math. Tong Mao, Ding-Xuan Zhou
2022	J	`jnl`	Attention Enables Zero Approximation Error. CoRR Zhiying Fang, Yidong Ouyang, Ding-Xuan Zhou, Guang Cheng
2022	J	`jnl`	CNN models for readability of Chinese texts. Math. Found. Comput. Han Feng, Sizai Hou, Le-Yin Wei, Ding-Xuan Zhou
2022	J	`jnl`	Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. IEEE Trans. Pattern Anal. Mach. Intell. Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou
2022	J	`jnl`	Differentially Private Stochastic Gradient Descent with Low-Noise. CoRR Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou
2022	—	`conf`	Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression. EMNLP (Findings) Jinshan Zeng, Yudong Xie, Xianglong Yu, John Lee, Ding-Xuan Zhou
2022	J	`jnl`	Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints. J. Sci. Comput. Jinshan Zeng, Wotao Yin, Ding-Xuan Zhou
2022	J	`jnl`	Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data. IEEE Trans. Neural Networks Learn. Syst. Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou
2022	A*	`conf`	Stability and Generalization for Markov Chain Stochastic Gradient Methods. NeurIPS Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou
2022	J	`jnl`	Stability and Generalization for Markov Chain Stochastic Gradient Methods. CoRR Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou
2022	J	`jnl`	Universal Consistency of Deep Convolutional Neural Networks. IEEE Trans. Inf. Theory Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou
2021	J	`jnl`	Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere. SIAM J. Numer. Anal. Shaobo Lin, Yuguang Wang, Ding-Xuan Zhou
2021	J	`jnl`	Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets. CoRR Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou
2021	J	`jnl`	Kernel gradient descent algorithm for information theoretic learning. J. Approx. Theory Ting Hu, Qiang Wu, Ding-Xuan Zhou
2021	J	`jnl`	On ADMM in Deep Learning: Convergence and Saturation-Avoidance. J. Mach. Learn. Res. Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou
2021	J	`jnl`	Radial Basis Function Approximation with Distributively Stored Data on Spheres. CoRR Han Feng, Shao-Bo Lin, Ding-Xuan Zhou
2021	J	`jnl`	Robust Kernel-based Distribution Regression. CoRR Zhan Yu, Daniel W. C. Ho, Ding-Xuan Zhou
2021	J	`jnl`	Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions. CoRR Tong Mao, Zhongjie Shi, Ding-Xuan Zhou
2021	J	`jnl`	Theory of deep convolutional neural networks III: Approximating radial functions. Neural Networks Tong Mao, Zhongjie Shi, Ding-Xuan Zhou
2021	A*	`conf`	Towards Understanding the Spectral Bias of Deep Learning. IJCAI Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
2021	J	`jnl`	Universal Consistency of Deep Convolutional Neural Networks. CoRR Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou
2020	J	`jnl`	Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. CoRR Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou
2020	J	`jnl`	Distributed Kernel Ridge Regression with Communications. CoRR Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
2020	J	`jnl`	Distributed Kernel Ridge Regression with Communications. J. Mach. Learn. Res. Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
2020	J	`jnl`	Optimal learning rates for distribution regression. J. Complex. Zhiying Fang, Zheng-Chu Guo, Ding-Xuan Zhou
2020	J	`jnl`	Theory of Deep Convolutional Neural Networks II: Spherical Analysis. CoRR Zhiying Fang, Han Feng, Shuo Huang, Ding-Xuan Zhou
2020	J	`jnl`	Theory of deep convolutional neural networks II: Spherical analysis. Neural Networks Zhiying Fang, Han Feng, Shuo Huang, Ding-Xuan Zhou
2020	J	`jnl`	Theory of deep convolutional neural networks: Downsampling. Neural Networks Ding-Xuan Zhou
2019	J	`jnl`	Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. J. Mach. Learn. Res. Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou
2019	J	`jnl`	Data-Dependent Generalization Bounds for Multi-Class Classification. IEEE Trans. Inf. Theory Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft
2019	J	`jnl`	Deep Net Tree Structure for Balance of Capacity and Approximation Ability. Frontiers Appl. Math. Stat. Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
2019	J	`jnl`	Deep Neural Networks for Rotation-Invariance Approximation and Learning. CoRR Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
2019	J	`jnl`	Distributed filtered hyperinterpolation for noisy data on the sphere. CoRR Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou
2019	J	`jnl`	Fast Polynomial Kernel Classification for Massive Data. CoRR Jinshan Zeng, Minrun Wu, Shao-Bo Lin, Ding-Xuan Zhou
2019	A*	`conf`	Optimal Stochastic and Online Learning with Individual Iterates. NeurIPS Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou
2019	J	`jnl`	Realization of spatial sparseness by deep ReLU nets with massive data. CoRR Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou
2019	J	`jnl`	Towards Understanding the Spectral Bias of Deep Learning. CoRR Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
2018	J	`jnl`	Construction of Neural Networks for Realization of Localized Deep Learning. Frontiers Appl. Math. Stat. Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
2018	J	`jnl`	Construction of neural networks for realization of localized deep learning. CoRR Charles K. Chui, Shaobo Lin, Ding-Xuan Zhou
2018	J	`jnl`	Convergence of Online Mirror Descent Algorithms. CoRR Yunwen Lei, Ding-Xuan Zhou
2018	J	`jnl`	Learning Theory of Randomized Sparse Kaczmarz Method. SIAM J. Imaging Sci. Yunwen Lei, Ding-Xuan Zhou
2018	J	`jnl`	Modified Fejér sequences and applications. Comput. Optim. Appl. Junhong Lin, Lorenzo Rosasco, Silvia Villa, Ding-Xuan Zhou
2018	J	`jnl`	Online Learning Algorithms Can Converge Comparably Fast as Batch Learning. IEEE Trans. Neural Networks Learn. Syst. Junhong Lin, Ding-Xuan Zhou
2018	J	`jnl`	Total stability of kernel methods. Neurocomputing Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou
2018	J	`jnl`	Universality of Deep Convolutional Neural Networks. CoRR Ding-Xuan Zhou
2017	J	`jnl`	Analysis of Online Composite Mirror Descent Algorithm. Neural Comput. Yunwen Lei, Ding-Xuan Zhou
2017	J	`jnl`	Approximation on variable exponent spaces by linear integral operators. J. Approx. Theory Bing-Zheng Li, Bo-Lu He, Ding-Xuan Zhou
2017	J	`jnl`	Distributed Learning with Regularized Least Squares. J. Mach. Learn. Res. Shaobo Lin, Xin Guo, Ding-Xuan Zhou
2017	J	`jnl`	Distributed Semi-supervised Learning with Kernel Ridge Regression. J. Mach. Learn. Res. Xiangyu Chang, Shaobo Lin, Ding-Xuan Zhou
2017	J	`jnl`	Generalization Error Bounds for Extreme Multi-class Classification. CoRR Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft
2017	J	`jnl`	Online pairwise learning algorithms with convex loss functions. Inf. Sci. Junhong Lin, Yunwen Lei, Bo Zhang, Ding-Xuan Zhou
2017	J	`jnl`	Online regularized learning with pairwise loss functions. Adv. Comput. Math. Zheng-Chu Guo, Yiming Ying, Ding-Xuan Zhou
2017	J	`jnl`	Total stability of kernel methods. CoRR Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou
2016	J	`jnl`	Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression. IEEE Trans. Signal Process. Ting Hu, Qiang Wu, Ding-Xuan Zhou
2016	J	`jnl`	Distributed Learning with Regularized Least Squares. CoRR Shaobo Lin, Xin Guo, Ding-Xuan Zhou
2016	A	`conf`	Fast Convergence of Online Pairwise Learning Algorithms. AISTATS Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou
2016	J	`jnl`	Iterative Regularization for Learning with Convex Loss Functions. J. Mach. Learn. Res. Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou
2016	J	`jnl`	On the robustness of regularized pairwise learning methods based on kernels. J. Complex. Andreas Christmann, Ding-Xuan Zhou
2016	J	`jnl`	Online Pairwise Learning Algorithms. Neural Comput. Yiming Ying, Ding-Xuan Zhou
2016	J	`jnl`	Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes. J. Mach. Learn. Res. Xin Guo, Jun Fan, Ding-Xuan Zhou
2015	J	`jnl`	Learning theory of randomized Kaczmarz algorithm. J. Mach. Learn. Res. Junhong Lin, Ding-Xuan Zhou
2015	J	`jnl`	Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition. CoRR Ming Yuan, Ding-Xuan Zhou
2015	J	`jnl`	Online Pairwise Learning Algorithms with Kernels. CoRR Yiming Ying, Ding-Xuan Zhou
2015	J	`jnl`	Unregularized Online Learning Algorithms with General Loss Functions. CoRR Yiming Ying, Ding-Xuan Zhou
2014	J	`jnl`	Asymptotic Behaviour of Extinction Probability of Interacting Branching Collision Processes. J. Appl. Probab. Anyue Chen, Junping Li, Yiqing Chen, Ding-Xuan Zhou
2014	J	`jnl`	Consistency Analysis of an Empirical Minimum Error Entropy Algorithm. CoRR Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou
2013	J	`jnl`	An approximation theory approach to learning with J. Approx. Theory Hong-Yan Wang, Quan-Wu Xiao, Ding-Xuan Zhou
2013	J	`jnl`	Concentration estimates for learning with unbounded sampling. Adv. Comput. Math. Zheng-Chu Guo, Ding-Xuan Zhou
2013	J	`jnl`	Learning theory approach to minimum error entropy criterion. J. Mach. Learn. Res. Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou
2012	J	`jnl`	Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression. J. Appl. Math. Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou
2012	J	`jnl`	Learning Theory Approach to Minimum Error Entropy Criterion CoRR Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou
2011	J	`jnl`	Learning with varying insensitive loss. Appl. Math. Lett. Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou
2011	J	`jnl`	Normal estimation on manifolds by gradient learning. Numer. Linear Algebra Appl. Lei Shi, Ding-Xuan Zhou
2011	J	`jnl`	Optimal learning rates for least squares regularized regression with unbounded sampling. J. Complex. Cheng Wang, Ding-Xuan Zhou
2010	J	`jnl`	Hermite learning with gradient data. J. Comput. Appl. Math. Lei Shi, Xin Guo, Ding-Xuan Zhou
2010	J	`jnl`	Moving least-square method in learning theory. J. Approx. Theory Hong-Yan Wang, Dao-Hong Xiang, Ding-Xuan Zhou
2009	J	`jnl`	Classification with Gaussians and Convex Loss. J. Mach. Learn. Res. Dao-Hong Xiang, Ding-Xuan Zhou
2009	J	`jnl`	Gradient learning in a classification setting by gradient descent. J. Approx. Theory Jia Cai, Hongyan Wang, Ding-Xuan Zhou
2009	J	`jnl`	High order Parzen windows and randomized sampling. Adv. Comput. Math. Xiang-Jun Zhou, Ding-Xuan Zhou
2009	J	`jnl`	Online Learning with Samples Drawn from Non-identical Distributions. J. Mach. Learn. Res. Ting Hu, Ding-Xuan Zhou
2008	J	`jnl`	Learning and approximation by Gaussians on Riemannian manifolds. Adv. Comput. Math. Gui-Bo Ye, Ding-Xuan Zhou
2008	J	`jnl`	Learning with sample dependent hypothesis spaces. Comput. Math. Appl. Qiang Wu, Ding-Xuan Zhou
2008	J	`jnl`	Parzen windows for multi-class classification. J. Complex. Zhi-Wei Pan, Dao-Hong Xiang, Quan-Wu Xiao, Ding-Xuan Zhou
2007	J	`jnl`	Learnability of Gaussians with Flexible Variances. J. Mach. Learn. Res. Yiming Ying, Ding-Xuan Zhou
2007	J	`jnl`	Multi-kernel regularized classifiers. J. Complex. Qiang Wu, Yiming Ying, Ding-Xuan Zhou
2006	J	`jnl`	Approximation with polynomial kernels and SVM classifiers. Adv. Comput. Math. Ding-Xuan Zhou, Kurt Jetter
2006	J	`jnl`	Learning Coordinate Covariances via Gradients. J. Mach. Learn. Res. Sayan Mukherjee, Ding-Xuan Zhou
2006	J	`jnl`	Learning Rates of Least-Square Regularized Regression. Found. Comput. Math. Qiang Wu, Yiming Ying, Ding-Xuan Zhou
2006	J	`jnl`	Online Regularized Classification Algorithms. IEEE Trans. Inf. Theory Yiming Ying, Ding-Xuan Zhou
2005	J	`jnl`	SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming. Neural Comput. Qiang Wu, Ding-Xuan Zhou
2004	J	`jnl`	Modeling Language Evolution. Found. Comput. Math. Felipe Cucker, Steve Smale, Ding-Xuan Zhou
2004	J	`jnl`	Support Vector Machine Soft Margin Classifiers: Error Analysis. J. Mach. Learn. Res. Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou
2003	J	`jnl`	Capacity of reproducing kernel spaces in learning theory. IEEE Trans. Inf. Theory Ding-Xuan Zhou
2003	J	`jnl`	Properties of locally linearly independent refinable function vectors. J. Approx. Theory Gerlind Plonka, Ding-Xuan Zhou
2002	J	`jnl`	Interpolatory orthogonal multiwavelets and refinable functions. IEEE Trans. Signal Process. Ding-Xuan Zhou
2002	J	`jnl`	Supports of Locally Linearly Independent Adv. Comput. Math. Hoi Ling Cheung, Canqin Tang, Ding-Xuan Zhou
2002	J	`jnl`	The covering number in learning theory. J. Complex. Ding-Xuan Zhou
2001	J	`jnl`	Binomial Matrices. Adv. Comput. Math. Geoff Boyd, Charles A. Micchelli, Gilbert Strang, Ding-Xuan Zhou
2001	J	`jnl`	Self-Similar Lattice Tilings and Subdivision Schemes. SIAM J. Math. Anal. Ding-Xuan Zhou
2000	J	`jnl`	Convergence of Subdivision Schemes Associated with Nonnegative Masks. SIAM J. Matrix Anal. Appl. Rong-Qing Jia, Ding-Xuan Zhou
1999	J	`jnl`	Smoothness of Multiple Refinable Functions and Multiple Wavelets. SIAM J. Matrix Anal. Appl. Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou
1999	—	`conf`	System identification using wavelet neural networks. ECC Daniel W. C. Ho, Jinhua Xu, Ding-Xuan Zhou
1998	J	`jnl`	Vector subdivision schemes and multiple wavelets. Math. Comput. Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou