Ding-Xuan Zhou

According to our database, Ding-Xuan Zhou authored at least 123 papers between 1998 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Analysis of regularized federated learning.

Langming Liu, Ding-Xuan Zhou

Neurocomputing, 2025

2024

Deep Trajectory Recovery Approach of Offline Vehicles in the Internet of Vehicles.

Xiao Han, Ding-Xuan Zhou, Guojiang Shen, Xiangjie Kong, Yulong Zhao

IEEE Trans. Veh. Technol., November, 2024

Distributed Gradient Descent for Functional Learning.

Zhan Yu, Jun Fan, Zhongjie Shi, Ding-Xuan Zhou

IEEE Trans. Inf. Theory, September, 2024

SignReLU neural network and its approximation ability.

Jianfei Li, Han Feng, Ding-Xuan Zhou

J. Comput. Appl. Math., April, 2024

Sketching with Spherical Designs for Noisy Data Fitting on Spheres.

Shao-Bo Lin, Di Wang, Ding-Xuan Zhou

SIAM J. Sci. Comput., February, 2024

Classification with Deep Neural Networks and Logistic Loss.

Zihan Zhang, Lei Shi, Ding-Xuan Zhou

J. Mach. Learn. Res., 2024

Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks.

Yunfei Yang, Ding-Xuan Zhou

J. Mach. Learn. Res., 2024

Approximation of functions from Korobov spaces by shallow neural networks.

Yuqing Liu, Tong Mao, Ding-Xuan Zhou

Inf. Sci., 2024

Differentially private stochastic gradient descent with low-noise.

Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou

Neurocomputing, 2024

Spherical Analysis of Learning Nonlinear Functionals.

Zhenyu Yang, Shuo Huang, 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

Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks.

Guangrui Yang, Jianfei Li, Ming Li, 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

On the rates of convergence for learning with convolutional neural networks.

Yunfei Yang, Han Feng, Ding-Xuan Zhou

CoRR, 2024

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

2023

Rates of approximation by ReLU shallow neural networks.

Tong Mao, Ding-Xuan Zhou

J. Complex., December, 2023

Generalization Analysis of CNNs for Classification on Spheres.

Han Feng, Shuo Huang, Ding-Xuan Zhou

IEEE Trans. Neural Networks Learn. Syst., September, 2023

Approximation of smooth functionals using deep ReLU networks.

Linhao Song, Ying Liu, Jun Fan, Ding-Xuan Zhou

Neural Networks, September, 2023

Deep learning theory of distribution regression with CNNs.

Zhan Yu, Ding-Xuan Zhou

Adv. Comput. Math., August, 2023

Generalization Analysis of Pairwise Learning for Ranking With Deep Neural Networks.

Shuo Huang, Junyu Zhou, Han Feng, Ding-Xuan Zhou

Neural Comput., June, 2023

Federated learning for minimizing nonsmooth convex loss functions.

Le-Yin Wei, Zhan Yu, Ding-Xuan Zhou

Math. Found. Comput., 2023

Lifting the Veil: Unlocking the Power of Depth in Q-learning.

Shao-Bo Lin, Tao Li, Shaojie Tang, Yao Wang, Ding-Xuan Zhou

CoRR, 2023

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos.

Di Wang, Xiaotong 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, Chenyu Zeng, 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

Learning Theory of Distribution Regression with Neural Networks.

Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou

CoRR, 2023

Optimal Estimates for Pairwise Learning with Deep ReLU Networks.

Junyu Zhou, Shuo Huang, Han Feng, Ding-Xuan Zhou

CoRR, 2023

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks.

Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou

CoRR, 2023

Distributed Gradient Descent for Functional Learning.

Zhan Yu, Jun Fan, 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

Optimal rates of approximation by shallow ReLU^k neural networks and applications to nonparametric regression.

Yunfei Yang, Ding-Xuan Zhou

CoRR, 2023

Generalization Analysis for Contrastive Representation Learning.

Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou

Proceedings of the International Conference on Machine Learning, 2023

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

Universal Consistency of Deep Convolutional Neural Networks.

Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou

IEEE Trans. Inf. Theory, 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

CNN models for readability of Chinese texts.

Han Feng, Sizai Hou, Le-Yin Wei, Ding-Xuan Zhou

Math. Found. Comput., 2022

Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints.

Jinshan Zeng, Wotao Yin, Ding-Xuan Zhou

J. Sci. Comput., 2022

A new activation for neural networks and its approximation.

Jianfei Li, Han Feng, Ding-Xuan Zhou

CoRR, 2022

Approximation analysis of CNNs from feature extraction view.

Han Feng, Jianfei Li, Ding-Xuan Zhou

CoRR, 2022

Attention Enables Zero Approximation Error.

Zhiying Fang, Yidong Ouyang, Ding-Xuan Zhou, Guang Cheng

CoRR, 2022

Approximation of functions from Korobov spaces by deep convolutional neural networks.

Tong Mao, Ding-Xuan Zhou

Adv. Comput. Math., 2022

Stability and Generalization for Markov Chain Stochastic Gradient Methods.

Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression.

Jinshan Zeng, Yudong Xie, Xianglong Yu, John Lee, Ding-Xuan Zhou

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

2021

Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere.

Shaobo Lin, Yuguang Wang, Ding-Xuan Zhou

SIAM J. Numer. Anal., 2021

Theory of deep convolutional neural networks III: Approximating radial functions.

Tong Mao, Zhongjie Shi, Ding-Xuan Zhou

Neural Networks, 2021

On ADMM in Deep Learning: Convergence and Saturation-Avoidance.

Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou

J. Mach. Learn. Res., 2021

Kernel gradient descent algorithm for information theoretic learning.

Ting Hu, Qiang Wu, Ding-Xuan Zhou

J. Approx. Theory, 2021

Radial Basis Function Approximation with Distributively Stored Data on Spheres.

Han Feng, Shao-Bo Lin, Ding-Xuan Zhou

CoRR, 2021

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

Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou

CoRR, 2021

Robust Kernel-based Distribution Regression.

Zhan Yu, Daniel W. C. Ho, Ding-Xuan Zhou

CoRR, 2021

Towards Understanding the Spectral Bias of Deep Learning.

Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020

Theory of deep convolutional neural networks: Downsampling.

Ding-Xuan Zhou

Neural Networks, 2020

Theory of deep convolutional neural networks II: Spherical analysis.

Zhiying Fang, Han Feng, Shuo Huang, Ding-Xuan Zhou

Neural Networks, 2020

Distributed Kernel Ridge Regression with Communications.

Shao-Bo Lin, Di Wang, Ding-Xuan Zhou

J. Mach. Learn. Res., 2020

Optimal learning rates for distribution regression.

Zhiying Fang, Zheng-Chu Guo, Ding-Xuan Zhou

J. Complex., 2020

2019

Data-Dependent Generalization Bounds for Multi-Class Classification.

Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft

IEEE Trans. Inf. Theory, 2019

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping.

Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou

J. Mach. Learn. Res., 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

Fast Polynomial Kernel Classification for Massive Data.

Jinshan Zeng, Minrun Wu, Shao-Bo Lin, Ding-Xuan Zhou

CoRR, 2019

Deep Neural Networks for Rotation-Invariance Approximation and Learning.

Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou

CoRR, 2019

Optimal Stochastic and Online Learning with Individual Iterates.

Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Online Learning Algorithms Can Converge Comparably Fast as Batch Learning.

Junhong Lin, Ding-Xuan Zhou

IEEE Trans. Neural Networks Learn. Syst., 2018

Learning Theory of Randomized Sparse Kaczmarz Method.

Yunwen Lei, Ding-Xuan Zhou

SIAM J. Imaging Sci., 2018

Total stability of kernel methods.

Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou

Neurocomputing, 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

Universality of Deep Convolutional Neural Networks.

Ding-Xuan Zhou

CoRR, 2018

Convergence of Online Mirror Descent Algorithms.

Yunwen Lei, Ding-Xuan Zhou

CoRR, 2018

Modified Fejér sequences and applications.

Junhong Lin, Lorenzo Rosasco, Silvia Villa, Ding-Xuan Zhou

Comput. Optim. Appl., 2018

2017

Analysis of Online Composite Mirror Descent Algorithm.

Yunwen Lei, Ding-Xuan Zhou

Neural Comput., 2017

Distributed Learning with Regularized Least Squares.

Shaobo Lin, Xin Guo, Ding-Xuan Zhou

J. Mach. Learn. Res., 2017

Distributed Semi-supervised Learning with Kernel Ridge Regression.

Xiangyu Chang, Shaobo Lin, Ding-Xuan Zhou

J. Mach. Learn. Res., 2017

Approximation on variable exponent spaces by linear integral operators.

Bing-Zheng Li, Bo-Lu He, Ding-Xuan Zhou

J. Approx. Theory, 2017

Online pairwise learning algorithms with convex loss functions.

Junhong Lin, Yunwen Lei, Bo Zhang, Ding-Xuan Zhou

Inf. Sci., 2017

Generalization Error Bounds for Extreme Multi-class Classification.

Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft

CoRR, 2017

Online regularized learning with pairwise loss functions.

Zheng-Chu Guo, Yiming Ying, Ding-Xuan Zhou

Adv. Comput. Math., 2017

2016

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

Ting Hu, Qiang Wu, Ding-Xuan Zhou

IEEE Trans. Signal Process., 2016

Online Pairwise Learning Algorithms.

Yiming Ying, Ding-Xuan Zhou

Neural Comput., 2016

Iterative Regularization for Learning with Convex Loss Functions.

Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou

J. Mach. Learn. Res., 2016

Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes.

Xin Guo, Jun Fan, Ding-Xuan Zhou

J. Mach. Learn. Res., 2016

On the robustness of regularized pairwise learning methods based on kernels.

Andreas Christmann, Ding-Xuan Zhou

J. Complex., 2016

Fast Convergence of Online Pairwise Learning Algorithms.

Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Learning theory of randomized Kaczmarz algorithm.

Junhong Lin, Ding-Xuan Zhou

J. Mach. Learn. Res., 2015

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

Ming Yuan, Ding-Xuan Zhou

CoRR, 2015

Unregularized Online Learning Algorithms with General Loss Functions.

Yiming Ying, Ding-Xuan Zhou

CoRR, 2015

Online Pairwise Learning Algorithms with Kernels.

Yiming Ying, Ding-Xuan Zhou

CoRR, 2015

2014

Asymptotic Behaviour of Extinction Probability of Interacting Branching Collision Processes.

Anyue Chen, Junping Li, Yiqing Chen, Ding-Xuan Zhou

J. Appl. Probab., 2014

Consistency Analysis of an Empirical Minimum Error Entropy Algorithm.

Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou

CoRR, 2014

2013

Learning theory approach to minimum error entropy criterion.

Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou

J. Mach. Learn. Res., 2013

An approximation theory approach to learning with <i>ℓ</i>¹ regularization.

Hong-Yan Wang, Quan-Wu Xiao, Ding-Xuan Zhou

J. Approx. Theory, 2013

Concentration estimates for learning with unbounded sampling.

Zheng-Chu Guo, Ding-Xuan Zhou

Adv. Comput. Math., 2013

2012

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

Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou

J. Appl. Math., 2012

2011

Normal estimation on manifolds by gradient learning.

Lei Shi, Ding-Xuan Zhou

Numer. Linear Algebra Appl., 2011

Optimal learning rates for least squares regularized regression with unbounded sampling.

Cheng Wang, Ding-Xuan Zhou

J. Complex., 2011

Learning with varying insensitive loss.

Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou

Appl. Math. Lett., 2011

2010

Hermite learning with gradient data.

Lei Shi, Xin Guo, Ding-Xuan Zhou

J. Comput. Appl. Math., 2010

Moving least-square method in learning theory.

Hong-Yan Wang, Dao-Hong Xiang, Ding-Xuan Zhou

J. Approx. Theory, 2010

2009

Classification with Gaussians and Convex Loss.

Dao-Hong Xiang, Ding-Xuan Zhou

J. Mach. Learn. Res., 2009

Online Learning with Samples Drawn from Non-identical Distributions.

Ting Hu, Ding-Xuan Zhou

J. Mach. Learn. Res., 2009

Gradient learning in a classification setting by gradient descent.

Jia Cai, Hongyan Wang, Ding-Xuan Zhou

J. Approx. Theory, 2009

High order Parzen windows and randomized sampling.

Xiang-Jun Zhou, Ding-Xuan Zhou

Adv. Comput. Math., 2009

2008

Parzen windows for multi-class classification.

Zhi-Wei Pan, Dao-Hong Xiang, Quan-Wu Xiao, Ding-Xuan Zhou

J. Complex., 2008

Learning with sample dependent hypothesis spaces.

Qiang Wu, Ding-Xuan Zhou

Comput. Math. Appl., 2008

Learning and approximation by Gaussians on Riemannian manifolds.

Gui-Bo Ye, Ding-Xuan Zhou

Adv. Comput. Math., 2008

2007

Learnability of Gaussians with Flexible Variances.

Yiming Ying, Ding-Xuan Zhou

J. Mach. Learn. Res., 2007

Multi-kernel regularized classifiers.

Qiang Wu, Yiming Ying, Ding-Xuan Zhou

J. Complex., 2007

2006

Online Regularized Classification Algorithms.

Yiming Ying, Ding-Xuan Zhou

IEEE Trans. Inf. Theory, 2006

Learning Coordinate Covariances via Gradients.

Sayan Mukherjee, Ding-Xuan Zhou

J. Mach. Learn. Res., 2006

Learning Rates of Least-Square Regularized Regression.

Qiang Wu, Yiming Ying, Ding-Xuan Zhou

Found. Comput. Math., 2006

Approximation with polynomial kernels and SVM classifiers.

Ding-Xuan Zhou, Kurt Jetter

Adv. Comput. Math., 2006

2005

SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming.

Qiang Wu, Ding-Xuan Zhou

Neural Comput., 2005

2004

Support Vector Machine Soft Margin Classifiers: Error Analysis.

Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou

J. Mach. Learn. Res., 2004

Modeling Language Evolution.

Felipe Cucker, Steve Smale, Ding-Xuan Zhou

Found. Comput. Math., 2004

2003

Capacity of reproducing kernel spaces in learning theory.

Ding-Xuan Zhou

IEEE Trans. Inf. Theory, 2003

Properties of locally linearly independent refinable function vectors.

Gerlind Plonka, Ding-Xuan Zhou

J. Approx. Theory, 2003

2002

Interpolatory orthogonal multiwavelets and refinable functions.

Ding-Xuan Zhou

IEEE Trans. Signal Process., 2002

The covering number in learning theory.

Ding-Xuan Zhou

J. Complex., 2002

Supports of Locally Linearly Independent <i>M</i>-Refinable Functions, Attractors of Iterated Function Systems and Tilings.

Hoi Ling Cheung, Canqin Tang, Ding-Xuan Zhou

Adv. Comput. Math., 2002

2001

Self-Similar Lattice Tilings and Subdivision Schemes.

Ding-Xuan Zhou

SIAM J. Math. Anal., 2001

Binomial Matrices.

Geoff Boyd, Charles A. Micchelli, Gilbert Strang, Ding-Xuan Zhou

Adv. Comput. Math., 2001

2000

Convergence of Subdivision Schemes Associated with Nonnegative Masks.

Rong-Qing Jia, Ding-Xuan Zhou

SIAM J. Matrix Anal. Appl., 2000

1999

Smoothness of Multiple Refinable Functions and Multiple Wavelets.

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

SIAM J. Matrix Anal. Appl., 1999

System identification using wavelet neural networks.

Daniel W. C. Ho, Jinhua Xu, Ding-Xuan Zhou

Proceedings of the 5th European Control Conference, 1999

1998

Vector subdivision schemes and multiple wavelets.

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

Math. Comput., 1998