Ding-Xuan Zhou

ORCID

Publication Activity (10 Years)

Years Active: 1998-2024
Publications (10 Years): 92

Top Venues

CoRR

J. Mach. Learn. Res.

Neural Networks

IEEE Trans. Neural Networks Learn. Syst.

Publications

Jianfei Li, Han Feng, Ding-Xuan Zhou
SignReLU neural network and its approximation ability. J. Comput. Appl. Math. 440 (2024)
Zhongjie Shi, Jun Fan, Linhao Song, Ding-Xuan Zhou, Johan A. K. Suykens
Nonlinear functional regression by functional deep neural network with kernel embedding. CoRR (2024)
Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou
Differentially private stochastic gradient descent with low-noise. Neurocomputing 585 (2024)
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
Sketching with Spherical Designs for Noisy Data Fitting on Spheres. SIAM J. Sci. Comput. 46 (1) (2024)
Guangrui Yang, Jianfei Li, Ming Li, Han Feng, Ding-Xuan Zhou
Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks. CoRR (2024)
Yuqing Liu, Tong Mao, Ding-Xuan Zhou
Approximation of functions from Korobov spaces by shallow neural networks. Inf. Sci. 670 (2024)
Yunfei Yang, Han Feng, Ding-Xuan Zhou
On the rates of convergence for learning with convolutional neural networks. CoRR (2024)
Junyu Zhou, Puyu Wang, Ding-Xuan Zhou
Generalization analysis with deep ReLU networks for metric and similarity learning. CoRR (2024)
Tong Mao, Ding-Xuan Zhou
Rates of approximation by ReLU shallow neural networks. J. Complex. 79 (2023)
Zhan Yu, Jun Fan, Ding-Xuan Zhou
Distributed Gradient Descent for Functional Learning. CoRR (2023)
Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou
Learning Theory of Distribution Regression with Neural Networks. CoRR (2023)
Zhan Yu, Ding-Xuan Zhou
Deep learning theory of distribution regression with CNNs. Adv. Comput. Math. 49 (4) (2023)
Le-Yin Wei, Zhan Yu, Ding-Xuan Zhou
Federated learning for minimizing nonsmooth convex loss functions. Math. Found. Comput. 6 (4) (2023)
Tong Mao, Ding-Xuan Zhou
Rates of Approximation by ReLU Shallow Neural Networks. CoRR (2023)
Yunfei Yang, Ding-Xuan Zhou
neural networks and applications to nonparametric regression. CoRR (2023)
Puyu Wang, Yunwen Lei, Di Wang, Yiming Ying, Ding-Xuan Zhou
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks. CoRR (2023)
Guanhang Lei, Zhen Lei, Lei Shi, Chenyu Zeng, Ding-Xuan Zhou
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks. CoRR (2023)
Linhao Song, Jun Fan, Di-Rong Chen, Ding-Xuan Zhou
Approximation of Nonlinear Functionals Using Deep ReLU Networks. CoRR (2023)
Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
Generalization Analysis for Contrastive Representation Learning. CoRR (2023)
Shaobo Lin, Di Wang, Ding-Xuan Zhou
Sketching with Spherical Designs for Noisy Data Fitting on Spheres. CoRR (2023)
Linhao Song, Ying Liu, Jun Fan, Ding-Xuan Zhou
Approximation of smooth functionals using deep ReLU networks. Neural Networks 166 (2023)
Shao-Bo Lin, Tao Li, Shaojie Tang, Yao Wang, Ding-Xuan Zhou
Lifting the Veil: Unlocking the Power of Depth in Q-learning. CoRR (2023)
Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou
Generalization Analysis for Contrastive Representation Learning. ICML (2023)
Shuo Huang, Junyu Zhou, Han Feng, Ding-Xuan Zhou
Generalization Analysis of Pairwise Learning for Ranking With Deep Neural Networks. Neural Comput. 35 (6) (2023)
Zihan Zhang, Lei Shi, Ding-Xuan Zhou
Classification with Deep Neural Networks and Logistic Loss. CoRR (2023)
Di Wang, Xiaotong Liu, Shao-Bo Lin, Ding-Xuan Zhou
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos. CoRR (2023)
Zhi Han, Baichen Liu, Shao-Bo Lin, Ding-Xuan Zhou
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning. CoRR (2023)
Han Feng, Shuo Huang, Ding-Xuan Zhou
Generalization Analysis of CNNs for Classification on Spheres. IEEE Trans. Neural Networks Learn. Syst. 34 (9) (2023)
Junyu Zhou, Shuo Huang, Han Feng, Ding-Xuan Zhou
Optimal Estimates for Pairwise Learning with Deep ReLU Networks. CoRR (2023)
Yunfei Yang, Ding-Xuan Zhou
Nonparametric regression using over-parameterized shallow ReLU neural networks. CoRR (2023)
Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou
Universal Consistency of Deep Convolutional Neural Networks. IEEE Trans. Inf. Theory 68 (7) (2022)
Jinshan Zeng, Yudong Xie, Xianglong Yu, John Lee, Ding-Xuan Zhou
Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression. EMNLP (Findings) (2022)
Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou
Stability and Generalization for Markov Chain Stochastic Gradient Methods. NeurIPS (2022)
Han Feng, Jianfei Li, Ding-Xuan Zhou
Approximation analysis of CNNs from feature extraction view. CoRR (2022)
Zhiying Fang, Yidong Ouyang, Ding-Xuan Zhou, Guang Cheng
Attention Enables Zero Approximation Error. CoRR (2022)
Tong Mao, Ding-Xuan Zhou
Approximation of functions from Korobov spaces by deep convolutional neural networks. Adv. Comput. Math. 48 (6) (2022)
Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou
Stability and Generalization for Markov Chain Stochastic Gradient Methods. CoRR (2022)
Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou
Differentially Private Stochastic Gradient Descent with Low-Noise. CoRR (2022)
Jinshan Zeng, Wotao Yin, Ding-Xuan Zhou
Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints. J. Sci. Comput. 91 (2) (2022)
Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou
Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data. IEEE Trans. Neural Networks Learn. Syst. 33 (1) (2022)
Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. IEEE Trans. Pattern Anal. Mach. Intell. 44 (4) (2022)
Jianfei Li, Han Feng, Ding-Xuan Zhou
A new activation for neural networks and its approximation. CoRR (2022)
Han Feng, Sizai Hou, Le-Yin Wei, Ding-Xuan Zhou
CNN models for readability of Chinese texts. Math. Found. Comput. 5 (4) (2022)
Tong Mao, Zhongjie Shi, Ding-Xuan Zhou
Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions. CoRR (2021)
Ting Hu, Qiang Wu, Ding-Xuan Zhou
Kernel gradient descent algorithm for information theoretic learning. J. Approx. Theory 263 (2021)
Shaobo Lin, Yuguang Wang, Ding-Xuan Zhou
Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere. SIAM J. Numer. Anal. 59 (2) (2021)
Shao-Bo Lin, Kaidong Wang, Yao Wang, Ding-Xuan Zhou
Universal Consistency of Deep Convolutional Neural Networks. CoRR (2021)
Zhan Yu, Daniel W. C. Ho, Ding-Xuan Zhou
Robust Kernel-based Distribution Regression. CoRR (2021)
Han Feng, Shao-Bo Lin, Ding-Xuan Zhou
Radial Basis Function Approximation with Distributively Stored Data on Spheres. CoRR (2021)
Tong Mao, Zhongjie Shi, Ding-Xuan Zhou
Theory of deep convolutional neural networks III: Approximating radial functions. Neural Networks 144 (2021)
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
Towards Understanding the Spectral Bias of Deep Learning. IJCAI (2021)
Shao-Bo Lin, Yao Wang, Ding-Xuan Zhou
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets. CoRR (2021)
Jinshan Zeng, Shao-Bo Lin, Yuan Yao, Ding-Xuan Zhou
On ADMM in Deep Learning: Convergence and Saturation-Avoidance. J. Mach. Learn. Res. 22 (2021)
Zhiying Fang, Han Feng, Shuo Huang, Ding-Xuan Zhou
Theory of deep convolutional neural networks II: Spherical analysis. Neural Networks 131 (2020)
Zhi Han, Siquan Yu, Shao-Bo Lin, Ding-Xuan Zhou
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. CoRR (2020)
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
Distributed Kernel Ridge Regression with Communications. J. Mach. Learn. Res. 21 (2020)
Ding-Xuan Zhou
Theory of deep convolutional neural networks: Downsampling. Neural Networks 124 (2020)
Zhiying Fang, Han Feng, Shuo Huang, Ding-Xuan Zhou
Theory of Deep Convolutional Neural Networks II: Spherical Analysis. CoRR (2020)
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou
Distributed Kernel Ridge Regression with Communications. CoRR (2020)
Zhiying Fang, Zheng-Chu Guo, Ding-Xuan Zhou
Optimal learning rates for distribution regression. J. Complex. 56 (2020)
Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft
Data-Dependent Generalization Bounds for Multi-Class Classification. IEEE Trans. Inf. Theory 65 (5) (2019)
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Deep Neural Networks for Rotation-Invariance Approximation and Learning. CoRR (2019)
Charles K. Chui, Shao-Bo Lin, Bo Zhang, Ding-Xuan Zhou
Realization of spatial sparseness by deep ReLU nets with massive data. CoRR (2019)
Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. J. Mach. Learn. Res. 20 (2019)
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Deep Net Tree Structure for Balance of Capacity and Approximation Ability. Frontiers Appl. Math. Stat. 5 (2019)
Shao-Bo Lin, Yu Guang Wang, Ding-Xuan Zhou
Distributed filtered hyperinterpolation for noisy data on the sphere. CoRR (2019)
Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou
Optimal Stochastic and Online Learning with Individual Iterates. NeurIPS (2019)
Jinshan Zeng, Minrun Wu, Shao-Bo Lin, Ding-Xuan Zhou
Fast Polynomial Kernel Classification for Massive Data. CoRR (2019)
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
Towards Understanding the Spectral Bias of Deep Learning. CoRR (2019)
Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou
Total stability of kernel methods. Neurocomputing 289 (2018)
Charles K. Chui, Shaobo Lin, Ding-Xuan Zhou
Construction of neural networks for realization of localized deep learning. CoRR (2018)
Junhong Lin, Ding-Xuan Zhou
Online Learning Algorithms Can Converge Comparably Fast as Batch Learning. IEEE Trans. Neural Networks Learn. Syst. 29 (6) (2018)
Ding-Xuan Zhou
Universality of Deep Convolutional Neural Networks. CoRR (2018)
Yunwen Lei, Ding-Xuan Zhou
Learning Theory of Randomized Sparse Kaczmarz Method. SIAM J. Imaging Sci. 11 (1) (2018)
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
Construction of Neural Networks for Realization of Localized Deep Learning. Frontiers Appl. Math. Stat. 4 (2018)
Yunwen Lei, Ding-Xuan Zhou
Convergence of Online Mirror Descent Algorithms. CoRR (2018)
Junhong Lin, Lorenzo Rosasco, Silvia Villa, Ding-Xuan Zhou
Modified Fejér sequences and applications. Comput. Optim. Appl. 71 (1) (2018)
Xiangyu Chang, Shaobo Lin, Ding-Xuan Zhou
Distributed Semi-supervised Learning with Kernel Ridge Regression. J. Mach. Learn. Res. 18 (2017)
Zheng-Chu Guo, Yiming Ying, Ding-Xuan Zhou
Online regularized learning with pairwise loss functions. Adv. Comput. Math. 43 (1) (2017)
Bing-Zheng Li, Bo-Lu He, Ding-Xuan Zhou
Approximation on variable exponent spaces by linear integral operators. J. Approx. Theory 223 (2017)
Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou
Total stability of kernel methods. CoRR (2017)
Shaobo Lin, Xin Guo, Ding-Xuan Zhou
Distributed Learning with Regularized Least Squares. J. Mach. Learn. Res. 18 (2017)
Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft
Generalization Error Bounds for Extreme Multi-class Classification. CoRR (2017)
Yunwen Lei, Ding-Xuan Zhou
Analysis of Online Composite Mirror Descent Algorithm. Neural Comput. 29 (3) (2017)
Junhong Lin, Yunwen Lei, Bo Zhang, Ding-Xuan Zhou
Online pairwise learning algorithms with convex loss functions. Inf. Sci. 406 (2017)
Martin Boissier, Siwei Lyu, Yiming Ying, Ding-Xuan Zhou
Fast Convergence of Online Pairwise Learning Algorithms. AISTATS (2016)
Shaobo Lin, Xin Guo, Ding-Xuan Zhou
Distributed Learning with Regularized Least Squares. CoRR (2016)
Andreas Christmann, Ding-Xuan Zhou
On the robustness of regularized pairwise learning methods based on kernels. J. Complex. 37 (2016)
Ting Hu, Qiang Wu, Ding-Xuan Zhou
Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression. IEEE Trans. Signal Process. 64 (24) (2016)
Yiming Ying, Ding-Xuan Zhou
Online Pairwise Learning Algorithms. Neural Comput. 28 (4) (2016)
Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou
Iterative Regularization for Learning with Convex Loss Functions. J. Mach. Learn. Res. 17 (2016)
Xin Guo, Jun Fan, Ding-Xuan Zhou
Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes. J. Mach. Learn. Res. 17 (2016)
Yiming Ying, Ding-Xuan Zhou
Unregularized Online Learning Algorithms with General Loss Functions. CoRR (2015)
Junhong Lin, Ding-Xuan Zhou
Learning theory of randomized Kaczmarz algorithm. J. Mach. Learn. Res. 16 (2015)
Ming Yuan, Ding-Xuan Zhou
Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition. CoRR (2015)
Yiming Ying, Ding-Xuan Zhou
Online Pairwise Learning Algorithms with Kernels. CoRR (2015)
Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm. CoRR (2014)
Anyue Chen, Junping Li, Yiqing Chen, Ding-Xuan Zhou
Asymptotic Behaviour of Extinction Probability of Interacting Branching Collision Processes. J. Appl. Probab. 51 (1) (2014)
Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou
Learning theory approach to minimum error entropy criterion. J. Mach. Learn. Res. 14 (1) (2013)
Zheng-Chu Guo, Ding-Xuan Zhou
Concentration estimates for learning with unbounded sampling. Adv. Comput. Math. 38 (1) (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ding-Xuan Zhou