Publication Search Results
Exact matches for:
- Author = Zhou DX [web profile page]
1.
Liu P, Liu Y, Zhou X, Zhou DX
Peilin Liu, Yuqing Liu, Xiang Zhou, Ding-Xuan Zhou:
Approximation of functionals on Korobov spaces with Fourier Functional Networks,
Neural Networks,
182
(2025),
106922.
2.
Lei G, Lei Z, Zeng C, Zhou DX
Guanhang Lei, Zhen Lei, Lei Shi, Chenyu Zeng and Ding-Xuan Zhou:
Solving PDEs on spheres with physics-informed convolutional neural networks,
Applied and Computatonal Harmonic Analysis,
74
(2025),
no. January 2025,
Article 101714 (40 pages).
3.
Liu L, Zhou DX
Langming Liu, Ding-Xuan Zhou:
Analysis of regularized federated learning,
Neurocomputing,
611
(2025),
no. 1 January 2025,
Article 128579 (12 pages).
4.
Yang Y, Zhou DX
Yungei Yang, Ding-Xuan Zhou:
Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks,
Journal of Machine Learning Research,
25
(2024),
1–35.
5.
Zhang Z, Shi L, Zhou DX
Zihan Zhang, Lei Shi, Ding-Xuan Zhou:
Classification with Deep Neural Networks and Logistic Loss,
Journal of Machine Learning Research,
25
(2024),
1–117.
6.
Liu Y, Mao T, Zhou DX
Yuqing Liu, Tong Mao, Ding-Xuan Zhou:
Approximation of functions from Korobov spaces by shallow neural networks,
Information Sciences,
670
(2024),
120573 (13 pages).
7.
Wang P, Lei Y, Ying Y, Zhou DX
Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Differentially private stochastic gradient descent with low-noise,
Neurocomputing,
585
(2024),
Article 127557 (14 pages).
8.
Fang Q, Shi L, Xu M, Zhou DX
Qin Fang , Lei Shi, Min Xu and Ding-Xuan Zhou:
Efficient kernel canonical correlation analysis using Nyström approximation,
Inverse Problems,
40
(2024),
no. 4,
Article 045007 (26 pages).
9.
Lin SB, Wang D, Zhou DX
Shao-Bo Lin, Di Wang and Ding-Xuan Zhou:
Sketching with Spherical Designs for Noisy Data Fitting on Spheres,
SIAM Journal on Scientific Computing,
46
(2024),
no. 1,
A313–A337.
10.
Li J, Feng H, Zhou DX
Jianfei Li, Han Feng, Ding-Xuan Zhou:
SignReLU neural network and its approximation ability,
Journal of Computational and Applied Mathematics,
440
(2024),
no. April 2024,
Article number 115551 (23 pages).
11.
Wei LY, Yu Z, Zhou DX
Le-Yin Wei, Zhan Yu and Ding-Xuan Zhou:
Federated learning for minimizing nonsmooth convex loss functions,
Mathematical Foundations of Computing,
6
(2023),
no. 4,
753–770.
12.
Feng H, Huang S, Zhou DX
Han Feng, Shuo Huang and Ding-Xuan Zhou:
Generalization Analysis of CNNs for Classification on Spheres,
IEEE Transactions on Neural Networks and Learning Systems,
34
(2023),
no. 9,
6200–6213.
13.
Song L, Liu Y, Fan J, Zhou DX
Linhao Song, Ying Liu, Jun Fan, Ding-Xuan Zhou:
Approximation of smooth functionals using deep ReLU networks,
Neural Networks,
166
(2023),
424–436.
14.
Mao T, Zhou DX
Tong Mao, Ding-Xuan Zhou:
Rates of approximation by ReLU shallow neural networks,
Journal of Complexity,
79
(2023),
Article 101784 (21 pages).
15.
Mao T, Zhou DX
Tong Mao and Ding-Xuan Zhou:
Rates of approximation by ReLU shallow neural networks,
Journal of Complexity,
79
(2023),
Article 101784 (21 pages).
16.
Song L, Fan J, Chen DR, Zhou DX
Linhao Song, Jun Fan, Di-Rong Chen, Ding-Xuan Zhou:
Approximation of Nonlinear Functionals Using Deep ReLU Networks,
Journal of Fourier Analysis and Applications,
29
(2023),
no. 4,
Article 50 (23 pages).
17.
Lei Y, Yang T, Ying Y, Zhou DX
Yunwen Lei, Tianbao Yang, Yiming Ying, Ding-Xuan Zhou:
Generalization Analysis for Contrastive Representation Learning,
Proceedings of the 40th International Conference on Machine Learning,
ICML 2023 – Fortieth International Conference on Machine Learning,
ICML,
USA,
(2023),
28 pages.
18.
Yu Z, Zhou DX
Zhan Yu, Ding-Xuan Zhou:
Deep learning theory of distribution regression with CNNs,
Advances in Computational Mathematics,
49
(2023),
no. 4,
Article no. 5 (40 pages).
19.
Huang S, Zhou J, Feng H, Zhou DX
Shuo Huang, Junyu Zhou, Han Feng, Ding-Xuan Zhou:
Generalization Analysis of Pairwise Learning for Ranking With Deep Neural Networks,
Neural Computation,
35
(2023),
no. 6,
1135–1158.
20.
Mao T, Shi Z, Zhou DX
Tong Mao, Zhongjie Shi and Ding-Xuan Zhou:
Approximating functions with multi-features by deep convolutional neural networks,
Analysis and Applications,
21
(2023),
no. 1,
93–125.
21.
Guo X, Lin J, Zhou DX
Xin Guo, Junhong Lin Ding-XuanZhou:
Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces,
Applied and Computational Harmonic Analysis,
61
(2022),
288–318.
22.
Lin SB, Wang K, Wang Y, Zhou DX
Shao-Bo Lin, Kaidong Wang, Yao Wang and Ding-Xuan Zhou:
Universal Consistency of Deep Convolutional Neural Networks,
IEEE Transactions on Information Theory,
68
(2022),
no. 7,
4610–4617.
23.
Feng H, Hou S, Wei LY, Zhou DX
Han Feng, Sizai Hou, Le-Yin Wei, Ding-Xuan Zhou:
CNN models for readability of Chinese texts,
Mathematical Foundations of Computing,
5
(2022),
no. 4,
351–362.
24.
Wang P, Lei Y, Ying Y, Zhou DX
Puyu Wang, Yunwen Lei, Yiming Ying, Ding-Xuan Zhou:
Stability and generalization for Markov Chain stochastic gradient methods,
NeurIPS 2022,
Neural Information Processing Systems.Conference 36th 2022,
Koyejo, S. and Mohamed, S. et al. (eds.),
Neural Information Processing Systems Foundation, Inc. (NeurIPS),
USA,
(2022),
37 pages.
ISBN 9781713871088.
25.
Zeng J, Xie Y, Yu X, Lee JSY, Zhou DX
Jinshan Zeng, Yudong Xie, Xianglong Yu, John S. Y. Lee, Ding-Xuan Zhou:
Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression,
Proceedings of the EMNLP 2022,
EMNLP 2022,
ACL Anthology,
USA,
(2022),
4586–4597.
26.
Chui CK, Lin SB, Zhang B, Zhou DX
Charles K Chui, Shao-Bo Lin, Bo Zhang and Ding-Xuan Zhou:
Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data,
IEEE Transactions on Neural Networks and Learning Systems,
33
(2022),
229–243.
27.
Zeng J, Yin W, Zhou DX
Jinshan Zeng, Wotao Yin, Ding-Xuan Zhou:
Moreau Envelope Augmented Lagrangian Method for Nonconvex Nonsmooth Optimization with Linear Constraints,
Journal of Scientific Computing,
91
(2022),
no. 61,
43 pages.
28.
Han Z, Yu S, Lin SB, Zhou DX
Zhi Han, Siquan Yu, Shao-Bo Lin and Ding-Xuan Zhou:
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization,
IEEE Transactions on Pattern Analysis and Machine Intelligence,
44
(2022),
1853–1868.
29.
Mao T, Zhou DX
Tong Mao and Ding-Xuan Zhou:
Approximation of functions from Korobov spaces by deep convolutional neural networks,
Advances in Computational Mathematics,
48 (Open Access)
(2022),
no. 6,
Article 84 (26 pages).
30.
Mao T, Shi Z, Zhou DX
Tong Mao, Zhongjie Shi and Ding-Xuan Zhou:
Theory of Deep Convolutional Neural Networks III: Approximating radial functions,
Neural Networks,
144
(2021),
778–790.
31.
Hu T, Wu Q, Zhou DX
Ting Hu, Qiang Wu, Ding-Xuan Zhou:
Kernel gradient descent algorithm for information theoretic learning,
Journal of Approximation Theory,
263
(2021),
105518 (32 pages).
32.
Yu Z, Ho DWC, Shi Z, Zhou DX
Zhan Yu, Daniel W. C. Ho, Zhongjie Shi and Ding-Xuan Zhou:
Robust kernel-based distribution regression,
Inverse Problems,
37
(2021),
no. 2021,
105014 (14 pages).
33.
Zhou DX, Liu BJ
Ding-Xuan Zhou, Liu Bie Ju:
Deep Convolutional Neural Networks,
Wiley Encyclopedia of Electrical and Electronics Engineering,
000
(2021),
20 pages.
34.
Hu T, Zhou DX
Ting Hu, Ding-Xuan Zhou:
Distributed regularized least squares with flexible Gaussian kernels,
Applied and Computational Harmonic Analysis,
53
(2021),
349–377.
35.
Cao Y, Fang Z, Wu Y, Zhou DX, Gu Q
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou and Quanquan Gu:
Towards understanding the spectral bias of deep learning,
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence,
The Thirtieth International Joint Conference on Artificial Intelligence,
Zhi-Hua Zhou (ed.),
nternational Joint Conferences on Artifical Intelligence (IJCAI),
Montreal, Canada,
(2021),
7 pages.
ISBN 978-0-9992411-9-6.
36.
Zeng J, Lin SB, Yao Y, Zhou DX
Jinshan Zeng, Shao-Bo Lin, Yuan Yao and Ding-Xuan Zhou:
On ADMM in deep learning: convergence and saturation-avoidance,
Journal of Machine Learning Research,
22
(2021),
1–67.
37.
Lin SB, Wang YG, Zhou DX
Shao-Bo Lin, Yu Guang Wang and Ding-Xuan Zhou:
Distributed filtered hyperinterpolation for noisy data on the sphere,
SIAM Journal on Numerical Analysis (SINUM),
59
(2021),
634–659.
38.
Zhou DX
Ding-Xuan Zhou:
Universality of deep convolutional neural networks,
Applied and Computational Harmonic Analysis,
48
(2020),
787–794.
39.
Hu T, Wu Q, Zhou DX
Ting Hu, Qiang Wu, Ding-Xuan Zhou:
Distributed kernel gradient descent algorithm for minimum error entropy principle,
Applied and Computational Harmonic Analysis,
49
(2020),
229–256.
40.
Fang Z, Feng H, Huang S, Zhou DX
Zhiying Fang, Han Feng, Shuo Huang and Ding-Xuan Zhou:
Theory of deep convolutional neural networks II: Spherical analysis,
Neural Networks,
131
(2020),
154–162.
41.
Zhou DX
Ding-Xuan Zhou:
Theory of deep convolutional neural networks: Downsampling,
Neural Networks,
124
(2020),
319–327.
42.
Lin SB, Wang D, Zhou DX
Shao-Bo Lin, Di Wang, Ding-Xuan Zhou:
Distributed Kernel Ridge Regression with Communications,
Journal of Machine Learning Research,
21
(2020),
1–38.
43.
Lei Y, Zhou DX
Yunwen Lei, Ding-Xuan Zhou:
Convergence of online mirror descent,
Applied and Computational Harmonic Analysis,
48
(2020),
343–373.
44.
Fang Z, Guo ZC, Zhou DX
Zhiying Fang, Zheng-Chu Guo, Ding-Xuan Zhou:
Optimal learning rates for distribution regression,
Journal of Complexity,
56
(2020),
101426 (13 pages).
45.
Chui CK, Lin SB, Zhou DX
Charles K Chui, Shao-Bo Lin, Ding-Xuan Zhou:
Deep neural networks for rotation-invariance approximation and learning,
Analysis and Applications,
17
(2019),
737–772.
46.
Chui CK, Lin SB, Zhou DX
Charles K Chui, Shao-Bo Lin and Ding-Xuan Zhou:
Deep Net Tree Structure for Balance of Capacity and Approximation Ability,
Frontiers in Applied Mathematics and Statistics,
5
(2019),
no. 46,
11 pages.
47.
Lei Y, Yang P, Tang K, Zhou DX
Yunwen Lei, Peng Yang, Ke Tang, Ding-Xuan Zhou:
Optimal Stochastic and Online Learning with Individual Iterates,
NeurIPS Proceedings,
33rd Conference on Neural Information Processing Systems (NeurIPS 2019),,
H. Wallach and H. Larochelle and A. Beygelzimer and F. d'Alché-Buc and E. Fox and R. Garnett (eds.),
Advances in Neural Information Processing Systems,
Online Publication,
—,
(2019),
11 pages.
ISBN 9781713807933.
48.
Lei Y, Dogan U, Zhou DX, Kloft M
Yunwen Lei, Urun Dogan, Ding-Xuan Zhou and Marius Kloft:
Data-dependent Generalization Bounds for Multi-class Classification,
IEEE Transactions on Information Theory,
65
(2019),
2995–3021.
49.
Lin SB, Lei Y, Zhou DX
Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou:
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping,
Journal of Machine Learning Research,
20
(2019),
1–36.
50.
Lei Y, Zhou DX
Yunwen Lei and Ding-Xuan Zhou:
Analysis of Singular Value Thresholding Algorithm for Matrix Completion,
Journal of Fourier Analysis and Applications,
25
(2019),
2957–2972.
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