Publication Search Results
Exact matches for:
- Author = Zhou DX [web profile page]
1.
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).
2.
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).
3.
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.
4.
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.
5.
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).
6.
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).
7.
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).
8.
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.
9.
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).
10.
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.
11.
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.
12.
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.
13.
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).
14.
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).
15.
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).
16.
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.
17.
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).
18.
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.
19.
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.
20.
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.
21.
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.
22.
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.
23.
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.
24.
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.
25.
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.
26.
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.
27.
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.
28.
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).
29.
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.
30.
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).
31.
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).
32.
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.
33.
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.
34.
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.
35.
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.
36.
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.
37.
Zhou DX
Ding-Xuan Zhou:
Universality of deep convolutional neural networks,
Applied and Computational Harmonic Analysis,
48
(2020),
787–794.
38.
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.
39.
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.
40.
Zhou DX
Ding-Xuan Zhou:
Theory of deep convolutional neural networks: Downsampling,
Neural Networks,
124
(2020),
319–327.
41.
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.
42.
Lei Y, Zhou DX
Yunwen Lei, Ding-Xuan Zhou:
Convergence of online mirror descent,
Applied and Computational Harmonic Analysis,
48
(2020),
343–373.
43.
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).
44.
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.
45.
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.
46.
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.
47.
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.
48.
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.
49.
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.
50.
Lin SB, Zhou DX
Shao-Bo Lin, Ding-Xuan Zhou:
Optimal learning rates for kernel partial least squares,
Journal of Fourier Analysis and Applications,
24
(2018),
908–933.
51.
Zhou DX
Ding-Xuan Zhou:
Deep Distributed Convolutional Neural Networks: Universality,
Analysis and Applications,
16
(2018),
895–919.
52.
Lin J, Zhou DX
Junhong Lin and Ding-Xuan Zhou:
Online Learning Algorithms Can Converge Comparably Fast as Batch Learning,
IEEE Transactions on Neural Networks and Learning Systems,
29
(2018),
2367–2378.
53.
Christmann A, Xiang D, Zhou DX
Andreas Christmann, Daohong Xiang and Ding-Xuan Zhou:
Total stability of kernel methods,
Neurocomputing,
289
(2018),
101–118.
54.
Chui CK, Lin SB, Zhou DX
Charles K Chui, Shao-Bo Lin and Ding-Xuan Zhou:
Construction of neural networks for realization of localized deep learning,
Frontiers in Applied Mathematics and Statistics,
4
(2018),
Article 14: 11 pages.
55.
Lei Y, Zhou DX
Yunwen Lei and Ding-Xuan Zhou:
Learning theory of randomized Sparse Kaczmarz method,
SIAM Journal on Imaging Sciences,
11
(2018),
547–574.
56.
Lin J, Rosasco L, Villa S, Zhou DX
Junhong Lin, Lorenzo Rosasco, Silvia Villa and Ding-Xuan Zhou:
Modified Fejer Sequences and Applications,
Computational Optimization and Applications,
71
(2018),
95–113.
57.
Lin SB, Zhou DX
Shao-Bo Lin, Ding-Xuan Zhou:
Distributed kernel gradient descent algorithms,
Constructive Approximation,
47
(2018),
249–276.
58.
Ying Y, Zhou DX
Yiming Ying, Ding-Xuan Zhou:
Unregularized online learning algorithms with general loss functions,
Applied and Computational Harmonic Analysis,
42
(2017),
224–244.
59.
Guo ZC, Ying Y, Zhou DX
Zheng-Chu Guo, Yiming Ying, Ding-Xuan Zhou:
Online regularized learning with pairwise loss functions,
Advances in Computational Mathematics,
43
(2017),
127–150.
60.
Lin SB, Guo X, Zhou DX
Shao-Bo Lin, Xin Guo, Ding-Xuan Zhou:
Distributed learning with regularized least squares,
Journal of Machine Learning Research,
18
(2017),
no. 92,
1–31.
61.
Lei Y, Zhou DX
Yunwen Lei and Ding-Xuan Zhou:
Analysis of online composite mirror descent algorithm,
Neural Computation,
29
(2017),
825860.
62.
Guo ZC, Xiang DH, Guo X, Zhou DX
Zheng-Chu Guo, Dao-Hong Xiang, Xin Guo, Ding-Xuan Zhou:
Thresholded spectral algorithms for sparse approximations,
Analysis and Applications,
15
(2017),
433–455.
63.
Lin J, Lei Y, Zhang B, Zhou DX
Junhong Lin, Yunwen Lei, Bo Zhang and Ding-Xuan Zhou:
Online pairwise learning algorithms with convex loss functions,
Information Sciences,
406-407
(2017),
57–70.
64.
Chang X, Lin SB, Zhou DX
Xiangyu Chang, Shao-Bo Lin, Ding-Xuan Zhou:
Distributed semi-supervised learning with kernel ridge regression,
Journal of Machine Learning Research,
18
(2017),
no. 46,
1–22.
65.
Yuan M, Zhou DX
Ming Yuan, Ding-Xuan Zhou:
Approximation on variable exponent spaces by linear integral operators,
Journal of Approximation Theory 223 (2017), 29-51.,
223
(2017),
29–51.
66.
Guo ZC, Lin SB, Zhou DX
Zheng-Chu Guo, Shao-Bo Lin, Ding-Xuan Zhou:
Learning theory of distributed spectral algorithms,
Inverse Problems,
33
(2017),
074009 (29 pages).
67.
Yuan M, Zhou DX
Ming Yuan, Ding-Xuan Zhou:
Minimax Optimal rates of estimation in high dimensional additive models,
Annals of Statistics,
44
(2016),
2564–2593.
MR3576554
68.
Ying Y, Zhou DX
Yiming Ying and Ding-Xuan Zhou:
Online pairwise learning algorithms,
Neural Computation,
28
(2016),
743–777.
MR3867770
69.
Hu T, Wu Q, Zhou DX
Ting Hu, Qiang Wu, and Ding-Xuan Zhou:
Convergence of gradient descent for minimum error entropy principle in linear regression,
IEEE Transactions on Signal Processing,
64
(2016),
6571–6579.
MR3566620
70.
Christmann A, Zhou DX
Andreas Christmann and Ding-Xuan Zhou:
Learning rates for the risk of kernel-based quantile regression estimators in additive models,
Analysis and Applications,
14
(2016),
449–477.
MR3486095
71.
Lin J, Rosasco L, Zhou DX
Junhong Lin, Lorenzo Rosasco, Ding-Xuan Zhou:
Iterative regularization for learning with convex loss functions,
Journal of Machine Learning Research,
17
(2016),
no. 77,
1–38.
MR3517100
72.
Guo X, Fan J, Zhou DX
Xin Guo, Jun Fan, Ding-Xuan Zhou:
Sparsity and error analysis of empirical feature-based regularization schemes,
Journal of Machine Learning Research,
17
(2016),
no. 89,
1–34.
MR3543495
73.
Fan J, Hu T, Wu Q, Zhou DX
Jun Fan, Ting Hu, Qiang Wu, Ding-Xuan Zhou:
Consistency analysis of an empirical minimum error entropy algorithm,
Applied and Computational Harmonic Analysis 41,
41
(2016),
164–189.
MR3501996
74.
Christmann A, Zhou DX
Andreas Christmann and Ding-Xuan Zhou:
On the robustness of regularized pairwise learning methods based on kernels,
Journal of Complexity,
37
(2016),
1–33.
MR3550364
75.
Michelli CA, Pontil M, Wu Q, Zhou DX
Charles A Michelli, Massimiliano Pontil, Qiang Wu, Ding-Xuan Zhou:
Error bounds for learning the kernel,
Analysis and Applications,
14
(2016),
849–868.
MR3564937
76.
Lin J, Zhou DX
Junhong Lin, Ding-Xuan Zhou:
Learning theory of randomized Kaczmarz algorithm.,
J. Mach. Learn. Res.,
16
(2015),
3341–3365.
MR3450541
77.
Li L, Zhou DX
Luoqing Li, Ding-Xuan Zhou:
Learning theory approach to a system identification problem involving atomic norm.,
J. Fourier Anal. Appl.,
21
(2015),
no. 4,
#8211;753.
MR3370009
78.
Hu T, Fan J, Wu Q, Zhou DX
Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou:
Regularization schemes for minimum error entropy principle.,
Anal. Appl. (Singap.),
13
(2015),
no. 4,
#8211;455.
MR3340670
79.
Lin J, Zhou DX
Junhong Lin, Ding-Xuan Zhou:
Learning theory of randomized Kaczmarz algorithm.,
J. Mach. Learn. Res.,
16
(2015),
3341–3365.
MR3450541
80.
Li BZ, Zhou DX
Bing-Zheng Li, Ding-Xuan Zhou:
Analysis of approximation by linear operators on variable \(L_p^{p(\cdot)}\) spaces and applications in learning theory,
Abstract and Applied Analysis,
2014
(2014),
Article 54375 (10 pages).
MR3240539
81.
Chen A, Li J, Chen Y, Zhou DX
Anyue Chen, Junping Li, Yiqing Chen, Dingxuan Zhou:
Asymptotic behaviour of extinction probability of interacting branching collision processes.,
J. Appl. Probab.,
51
(2014),
no. 1,
#8211;234.
MR3189453
82.
Zhou DX
Ding-Xuan Zhou:
Approximation by positive linear operators on variable \(L^{p(\cdot)}\) spaces.,
J. Appl. Funct. Anal.,
9
(2014),
no. 3-4,
#8211;391.
MR3156221
83.
Zhou DX
Ding-Xuan Zhou:
Density problem and approximation error in learning theory,
Abstract and Applied Analysis,
2013
(2013),
Article 715683 (13 pages).
MR3111814
84.
Wang H, Xiao QW, Zhou DX
Hong-Yan Wang, Quan-Wu Xiao, Ding-Xuan Zhou:
An approximation theory approach to learning with \(\ell^1\) regularization.,
J. Approx. Theory,
167
(2013),
240–258.
MR3010046
85.
Gonska H, Prestin J, Tachev G, Zhou DX
Heiner Gonska, Jürgen Prestin, Gancho Tachev, Ding-xuan Zhou:
Simultaneous approximation by Bernstein operators in Hölder norms.,
Math. Nachr.,
286
(2013),
no. 4,
#8211;359.
MR3028779
86.
Hu T, Fan J, Wu Q, Zhou DX
Ting Hu, Jun Fan, Qiang Wu, Ding-Xuan Zhou:
Learning theory approach to minimum error entropy criterion.,
J. Mach. Learn. Res.,
14
(2013),
377–397.
MR3033336
87.
Guo ZC, Zhou DX
Zheng-Chu Guo, Ding-Xuan Zhou:
Concentration estimates for learning with unbounded sampling.,
Adv. Comput. Math.,
38
(2013),
no. 1,
#8211;223.
MR3011339
88.
Zhou DX
Ding-Xuan Zhou:
On grouping effect of elastic net.,
Statist. Probab. Lett.,
83
(2013),
no. 9,
–2112.
MR3079053
89.
Xiang DH, Hu T, Zhou DX
Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou:
Approximation analysis of learning algorithms for support vector regression and quantile regression,
Journal of Applied Mathematics,
2012
(2012),
Article 902139 (17 pages).
MR2880823
90.
Guo X, Zhou DX
Xin Guo, Ding-Xuan Zhou:
An empirical feature-based learning algorithm producing sparse approximations.,
Appl. Comput. Harmon. Anal.,
32
(2012),
no. 3,
#8211;400.
MR2892740
91.
Chen A, Li J, Chen Y, Zhou DX
Anyue Chen, Junping Li, Yiqing Chen, Dingxuan Zhou:
Extinction probability of interacting branching collision processes.,
Adv. in Appl. Probab.,
44
(2012),
no. 1,
#8211;259.
MR2951553
92.
Hu T, Xiang DH, Zhou DX
Ting Hu, Dao-Hong Xiang, Ding-Xuan Zhou:
Online learning for quantile regression and support vector regression.,
J. Statist. Plann. Inference,
142
(2012),
no. 12,
–3122.
MR2956797
93.
Xiang DH, Hu T, Zhou DX
Dao-Hong Xiang, Ting Hu, Ding-Xuan Zhou:
Learning with varying insensitive loss.,
Appl. Math. Lett.,
24
(2011),
no. 12,
–2109.
MR2826146
94.
Zhou DX, Shi L, Zhou DX
Xiang-Jun Zhou, Lei Shi, Ding-Xuan Zhou:
Non-uniform randomized sampling for multivariate approximation by high order Parzen windows.,
Canad. Math. Bull.,
54
(2011),
no. 3,
#8211;576.
MR2857989
95.
Shi L, Feng YL, Zhou DX
Lei Shi, Yun-Long Feng, Ding-Xuan Zhou:
Concentration estimates for learning with \(\ell^1\)-regularizer and data dependent hypothesis spaces.,
Appl. Comput. Harmon. Anal.,
31
(2011),
no. 2,
#8211;302.
MR2806485
96.
Shi L, Zhou DX
Lei Shi, Ding-Xuan Zhou:
Normal estimation on manifolds by gradient learning.,
Numer. Linear Algebra Appl.,
18
(2011),
no. 2,
#8211;259.
MR2791245
97.
Wang C, Zhou DX
Cheng Wang, Ding-Xuan Zhou:
Optimal learning rates for least squares regularized regression with unbounded sampling.,
J. Complexity,
27
(2011),
no. 1,
8211;67.
MR2745300
98.
Wang H, Xiang DH, Zhou DX
Hong-Yan Wang, Dao-Hong Xiang, Ding-Xuan Zhou:
Moving least-square method in learning theory.,
J. Approx. Theory,
162
(2010),
no. 3,
#8211;614.
MR2600986
99.
Xiao QW, Zhou DX
Quan-Wu Xiao, Ding-Xuan Zhou:
Learning by nonsymmetric kernels with data dependent spaces and \(\ell^1\)-regularizer.,
Taiwanese J. Math.,
14
(2010),
no. 5,
–1836.
MR2724135
100.
Hu T, Zhou DX
Ting Hu, Ding-Xuan Zhou:
Online classification with varying Gaussians.,
Stud. Appl. Math.,
124
(2010),
no. 1,
8211;83.
MR2589937
101.
Shi L, Guo X, Zhou DX
Lei Shi, Xin Guo, Ding-Xuan Zhou:
Hermite learning with gradient data.,
J. Comput. Appl. Math.,
233
(2010),
no. 11,
–3059.
MR2592278
102.
Mukherjee S, Wu Q, Zhou DX
Sayan Mukherjee, Qiang Wu, Ding-Xuan Zhou:
Learning gradients on manifolds.,
Bernoulli,
16
(2010),
no. 1,
#8211;207.
MR2648754
103.
Xiang DH, Zhou DX
Dao-Hong Xiang, Ding-Xuan Zhou:
Classification with Gaussians and convex loss.,
J. Mach. Learn. Res.,
10
(2009),
1447–1468.
MR2534867
104.
Cai J, Wang H, Zhou DX
Jia Cai, Hongyan Wang, Ding-Xuan Zhou:
Gradient learning in a classification setting by gradient descent.,
J. Approx. Theory,
161
(2009),
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Hu T, Zhou DX
Ting Hu, Ding-Xuan Zhou:
Online learning with samples drawn from non-identical distributions.,
J. Mach. Learn. Res.,
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MR2579915
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Ye GB, Zhou DX
Gui-Bo Ye, Ding-Xuan Zhou:
SVM learning and \(L^p\) approximation by Gaussians on Riemannian manifolds.,
Anal. Appl. (Singap.),
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Smale S, Zhou DX
Steve Smale, Ding-Xuan Zhou:
Online learning with Markov sampling.,
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MR2488871
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Zhou XJ, Zhou DX
Xiang-Jun Zhou, Ding-Xuan Zhou:
High order Parzen windows and randomized sampling.,
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Smale S, Zhou DX
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Geometry on probability spaces.,
Constr. Approx.,
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Wu Q, Zhou DX
Qiang Wu, Ding-Xuan Zhou:
Learning with sample dependent hypothesis spaces.,
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Zhou DX
Ding-Xuan Zhou:
Derivative reproducing properties for kernel methods in learning theory.,
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Dong X, Zhou DX
Xuemei Dong, Ding-Xuan Zhou:
Learning gradients by a gradient descent algorithm.,
J. Math. Anal. Appl.,
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MR2398266
113.
Pan ZW, Xiang DH, Xiao QW, Zhou DX
Zhi-Wei Pan, Dao-Hong Xiang, Quan-Wu Xiao, Ding-Xuan Zhou:
Parzen windows for multi-class classification.,
J. Complexity,
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MR2467590
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Ye GB, Zhou DX
Gui-Bo Ye, Ding-Xuan Zhou:
Learning and approximation by Gaussians on Riemannian manifolds.,
Adv. Comput. Math.,
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MR2438346
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Sun HW, Zhou DX
Hong-Wei Sun, Ding-Xuan Zhou:
Reproducing kernel Hilbert spaces associated with analytic translation-invariant Mercer kernels.,
J. Fourier Anal. Appl.,
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Cucker F, Zhou DX
Felipe Cucker, Ding Xuan Zhou:
Learning theory: an approximation theory viewpoint,
Cambridge Monographs on Applied and Computational Mathematics,
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Smale S, Zhou DX
Steve Smale, Ding-Xuan Zhou:
Learning theory estimates via integral operators and their approximations.,
Constr. Approx.,
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MR2327597
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Ying Y, Zhou DX
Yiming Ying, Ding-Xuan Zhou:
Learnability of Gaussians with flexible variances.,
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MR2320669
119.
Wu Q, Ying Y, Zhou DX
Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Multi-kernel regularized classifiers.,
J. Complexity,
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MR2297018
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Ye GB, Zhou DX
Gui-Bo Ye, Ding-Xuan Zhou:
Fully online classification by regularization.,
Appl. Comput. Harmon. Anal.,
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MR2344611
121.
Wu Q, Ying Y, Zhou DX
Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Learning theory: from regression to classification,
Studies in omputational Mathematics1,
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Ying Y, Zhou DX
Yiming Ying, Ding-Xuan Zhou:
Online regularized classification algorithms.,
IEEE Trans. Inform. Theory,
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MR2302601
123.
Mukherjee S, Zhou DX
Sayan Mukherjee, Ding-Xuan Zhou:
Learning coordinate covariances via gradients.,
J. Mach. Learn. Res.,
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Wu Q, Zhou DX
Qiang Wu, Ding-Xuan Zhou:
Analysis of support vector machine classification.,
J. Comput. Anal. Appl.,
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MR2225648
125.
Zhou DX, Jetter K
Ding-Xuan Zhou, Kurt Jetter:
Approximation with polynomial kernels and SVM classifiers.,
Adv. Comput. Math.,
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MR2231707
126.
Wu Q, Ying Y, Zhou DX
Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Learning rates of least-square regularized regression.,
Found. Comput. Math.,
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MR2228738
127.
Wu Q, Zhou DX
Qiang Wu, Ding-Xuan Zhou:
SVM soft margin classifiers: linear programming versus quadratic programming.,
Neural Comput.,
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MR2176072
128.
Smale S, Zhou DX
Steve Smale, Ding-Xuan Zhou:
Shannon sampling. II. Connections to learning theory.,
Appl. Comput. Harmon. Anal.,
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MR2186447
129.
Smale S, Zhou DX
Steve Smale, Ding-Xuan Zhou:
Shannon sampling and function reconstruction from point values.,
Bull. Amer. Math. Soc. (N.S.),
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MR2058288
130.
Cucker F, Smale S, Zhou DX
Felipe Cucker, Steve Smale, Ding-Xuan Zhou:
Modeling language evolution.,
Found. Comput. Math.,
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MR2078666
131.
Jia RQ, Wang JZ, Zhou DX
R.Q Jia, J Z Wang and D X Zhou:
Compactly supported wavelet bases for Sobolev spaces,
Applied and Computational Harmonic Analysis,
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MR2010944
132.
Micchelli CA, Zhou DX
Charles A. Micchelli, Ding-Xuan Zhou:
Refinable functions: positivity and interpolation.,
Anal. Appl. (Singap.),
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MR1993338
133.
Chen DR, Wu Q, Ying Y, Zhou DX
Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou:
Support vector machine soft margin classifiers: error analysis.,
J. Mach. Learn. Res.,
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MR2248013
134.
Zhou DX
Ding-Xuan Zhou:
Capacity of reproducing kernel spaces in learning theory.,
IEEE Trans. Inform. Theory,
49
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MR1985575
135.
Plonka G, Zhou DX
G. Plonka, D.-X. Zhou:
Properties of locally linearly independent refinable function vectors.,
J. Approx. Theory,
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MR1976123
136.
Jia RQ, Wang J, Zhou DX
Rong-Qing Jia, Jianzhong Wang, Ding-Xuan Zhou:
Compactly supported wavelet bases for Sobolev spaces.,
Appl. Comput. Harmon. Anal.,
15
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#8211;241.
MR2010944
137.
Smale S, Zhou DX
Steve Smale, Ding-Xuan Zhou:
Estimating the approximation error in learning theory.,
Anal. Appl. (Singap.),
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MR1959283
138.
Zhou DX
Ding-Xuan Zhou:
Two-scale homogeneous functions in wavelet analysis.,
J. Fourier Anal. Appl.,
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Cheung HL, Tang C, Zhou DX
Hoi Ling Cheung, Canqin Tang, Ding-Xuan Zhou:
Supports of locally linearly independent \(M\)-refinable functions, attractors of iterated function systems and tilings.,
Adv. Comput. Math.,
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MR1913388
140.
Zhou DX
Ding-Xuan Zhou:
Interpolatory orthogonal multiwavelets and refinable functions.,
IEEE Trans. Signal Process.,
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Zhou DX
Ding-Xuan Zhou:
The covering number in learning theory.,
J. Complexity,
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MR1928805
142.
Nielsen M, Zhou DX
Morten Nielsen, Ding-Xuan Zhou:
Mean size of wavelet packets.,
Appl. Comput. Harmon. Anal.,
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MR1930174
143.
Zhou DX
Ding-Xuan Zhou:
Self-similar lattice tilings and subdivision schemes.,
SIAM J. Math. Anal.,
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MR1857987
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Jia RQ, Lau KS, Zhou DX
Rong-Qing Jia, Ka-Sing Lau, Ding-Xuan Zhou:
\(L_p\) solutions of refinement equations.,
J. Fourier Anal. Appl.,
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MR1817673
145.
Strang G, Zhou DX
Gilbert Strang, Ding-Xuan Zhou:
The limits of refinable functions.,
Trans. Amer. Math. Soc.,
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MR1813602
146.
Zhou DX
Ding-Xuan Zhou:
Norms concerning subdivision sequences and their applications in wavelets.,
Appl. Comput. Harmon. Anal.,
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147.
Zhou DX
Ding-Xuan Zhou:
Spectra of subdivision operators.,
Proc. Amer. Math. Soc.,
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MR1784023
148.
Boyd G, Micchelli CA, Strang G, Zhou DX
Geoff Boyd, Charles A. Micchelli, Gilbert Strang, Ding-Xuan Zhou:
Binomial matrices.,
Adv. Comput. Math.,
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MR1865102
149.
Goodman TNT, Jia RQ, Zhou DX
T. N. T. Goodman, R.-Q. Jia, D.-X. Zhou:
Local linear independence of refinable vectors of functions.,
Proc. Roy. Soc. Edinburgh Sect. A,
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MR1776679
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Zhou DX
Ding-Xuan Zhou:
Multiple refinable Hermite interpolants.,
J. Approx. Theory,
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Cottin C, Gavrea I, Gonska HH, Kacso D, Zhou DX
C Cottin, I Gavrea, H H Gonska, D Kacso and D X Zhou:
Global smoothness preservation and the variation-diminishing property,
Journal of Inequalities and Applications,
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MR1733478
152.
Zhou DX
Ding-Xuan Zhou:
Solvability of linear systems of PDE's with constant coefficients.,
Proc. Amer. Math. Soc.,
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MR1476403
153.
Jia RQ, Riemenschneider SD, Zhou DX
Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou:
Smoothness of multiple refinable functions and multiple wavelets.,
SIAM J. Matrix Anal. Appl.,
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MR1709723
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Jia RQ, Zhou DX
Rong-Qing Jia, Ding-Xuan Zhou:
Convergence of subdivision schemes associated with nonnegative masks.,
SIAM J. Matrix Anal. Appl.,
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155.
Cottin C, Gavrea I, Gonska HH, Kacsó DP, Zhou DX
Claudia Cottin, Ioan Gavrea, Heinz H. Gonska, Daniela P. Kacsó, Ding-Xuan Zhou:
Global smoothness preservation and the variation-diminishing property.,
J. Inequal. Appl.,
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MR1733478
156.
Zhou DX
Ding-Xuan Zhou:
The \(p\)-norm joint spectral radius for even integers,
Methods and applications of analysis,
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MR1887522
157.
Strang G, Zhou DX
Gilbert Strang, Ding-Xuan Zhou:
Inhomogeneous refinement equations.,
J. Fourier Anal. Appl.,
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MR1666013
158.
Zhou DX
Ding-Xuan Zhou:
The \(p\)-norm joint spectral radius for even integers.,
Methods Appl. Anal.,
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MR1631335
159.
Jia RQ, Riemenschneider SD, Zhou DX
Rong-Qing Jia, S. D. Riemenschneider, Ding-Xuan Zhou:
Vector subdivision schemes and multiple wavelets.,
Math. Comp.,
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MR1484900
160.
Zhou DX
Ding-Xuan Zhou:
Some characterizations for box spline wavelets and linear Diophantine equations.,
Rocky Mountain J. Math.,
28
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MR1681681
161.
Zhou DX, Li BZ, Li S
Ding Xuan Zhou, Bing Zheng Li and Song Li:
A Korovkin theorem on approximation by linear operators in the space \(\sigma(L^\infty,L^1)\).,
Acta Math. Appl. Sinica,
20
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MR1623304
162.
Zhou DX
Ding-Xuan Zhou:
Existence of multiple refinable distributions,
Michigan Mathematical Journal,
44
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MR1460417
163.
Jia RQ, Riemenschneider SD, Zhou DX
R. Q. Jia, S. D. Riemenschneider, D. X. Zhou:
Approximation by multiple refinable functions.,
Canad. J. Math.,
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MR1604122
164.
Zhou DX
Ding-Xuan Zhou:
Existence of multiple refinable distributions.,
Michigan Math. J.,
44
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165.
Zhou DX
Ding-Xuan Zhou:
Extendibility of rational matrices.,
J. Approx. Theory,
88
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MR1429976
166.
Gonska HH, Zhou DX
Heinz H. Gonska, Ding-Xuan Zhou:
Design of Wilson-Fowler splines.,
Studia Univ. Babeş-Bolyai Math.,
42
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MR2362868
167.
Zhou DX
Ding-Xuan Zhou:
Linear dependence relations in wavelets and tilings.,
Linear Algebra Appl.,
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MR1417424
168.
Zhou DX
Ding-Xuan Zhou:
Stability of refinable functions, multiresolution analysis, and Haar bases.,
SIAM J. Math. Anal.,
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MR1382838
169.
Mache DH, Zhou DX
Detlef H. Mache, Ding X. Zhou:
Characterization theorems for the approximation by a family of operators.,
J. Approx. Theory,
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MR1370596
170.
Zhou DX
Ding-Xuan Zhou:
Box splines with rational directions and linear Diophantine equations.,
J. Math. Anal. Appl.,
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171.
Xuan PC, Zhou DX
P C Xuan, Ding-Xuan Zhou:
The order of convergence for weighted Baskakov operators,
Acta Math. Appl. Sinica,
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MR1338232
172.
Mache DH, Zhou DX
D. H. Mache, D. X. Zhou:
Best direct and converse results for Lagrange-type operators.,
Approx. Theory Appl. (N.S.),
11
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MR1370768
173.
Zhou DX
Ding Xuan Zhou:
On a problem of Gonska.,
Results Math.,
28
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MR1345365
174.
Zhou DX
Ding Xuan Zhou:
On smoothness characterized by Bernstein type operators.,
J. Approx. Theory,
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175.
Gonska HH, Zhou DX
Heinz H. Gonska, Ding Xuan Zhou:
On an extremal problem concerning Bernstein operators.,
Serdica Math. J.,
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176.
Zhou DX
Ding Xuan Zhou:
Construction of real-valued wavelets by symmetry.,
J. Approx. Theory,
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177.
Guo ZR, Zhou DX
Zhu Rui Guo, Ding Xuan Zhou:
Local approximation by modified Szász operators.,
J. Math. Anal. Appl.,
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178.
Gonska HH, Zhou DX
Heinz H. Gonska, Ding-Xuan Zhou:
Using wavelets for Szász-type operators.,
Rev. Anal. Numér. Théor. Approx.,
24
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MR1608455
179.
Jetter K, Zhou DX
Kurt Jetter, Ding-Xuan Zhou:
Seminorm and full norm order of linear approximation from shift-invariant spaces.,
Rend. Sem. Mat. Fis. Milano,
65
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277–302 (1997).
MR1459425
180.
Jetter K, Zhou DX
Kurt Jetter, Ding-Xuan Zhou:
Order of linear approximation from shift-invariant spaces.,
Constr. Approx.,
11
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MR1367171
181.
Gonska HH, Zhou DX
H. H. Gonska, D.-X. Zhou:
Local smoothness of functions and Bernstein-Durrmeyer operators.,
Comput. Math. Appl.,
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MR1343034
182.
Zhou DX, Jetter K
Ding Xuan Zhou, Kurt Jetter:
Characterisation of correctness of cardinal interpolation with shifted three-dimensional box splines.,
Proc. Roy. Soc. Edinburgh Sect. A,
125
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MR1361625
183.
Zhang NS, Zhou DX
N S Zhang, Ding-Xuan Zhou:
Direct and inverse theorems for Jackson operators in Besov spaces,
Acta Math. Appl. Sinica,
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MR1333921
184.
Zhou DX
Ding Xuan Zhou:
On wavelets in \(L_1\).,
Acta Math. Appl. Sinica (English Ser.),
10
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MR1270054
185.
Zhou DX, Zhang ZQ, Xuan PC
Ding Xuan Zhou, Zhen Qiu Zhang, Pei Cai Xuan:
On \(L_p\)-approximation by multidimensional Baskakov-Durrmeyer operators.,
Math. Appl. (Wuhan),
7
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MR1276223
186.
Zhou DX
Ding Xuan Zhou:
A note on derivatives of Bernstein polynomials.,
J. Approx. Theory,
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MR1284584
187.
Ye MD, Zhou DX
Mao Dong Ye, Ding Xuan Zhou:
A class of operators by means of three-diagonal matrices.,
J. Approx. Theory,
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188.
Zhou DX
Ding Xuan Zhou:
Weighted approximation by multidimensional Bernstein operators.,
J. Approx. Theory,
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189.
Zhou DX
Ding Xuan Zhou:
Weighted approximation by Szász-Mirakjan operators.,
J. Approx. Theory,
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190.
Zhou DX
Ding Xuan Zhou:
On a paper of Mazhar and Totik.,
J. Approx. Theory,
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MR1209969
191.
Zhou DX
Ding Xuan Zhou:
Converse theorems for multidimensional Kantorovich operators.,
Anal. Math.,
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MR1232056
192.
Zhou DX, Zhang NS
Ding Xuan Zhou, Nan Song Zhang:
Besov spaces of Ditzian-Totik type and Bernstein-Durrmeyer operators.,
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193.
Zhou DX
Ding-Xuan Zhou:
The rate of convergence for Bernstein operators with Jacobi weights,
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194.
Zhou DX
Ding Xuan Zhou:
Inverse theorems for multidimensional Bernstein-Durrmeyer operators in \(L_p\).,
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Guo ZR, Zhou DX
Zhu Rui Guo, Ding Xuan Zhou:
Approximation theorems for modified Szász operators.,
Acta Sci. Math. (Szeged),
56
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MR1222074
196.
Zhou DX
Ding Xuan Zhou:
A note on Bernstein type operators.,
Approx. Theory Appl.,
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MR1177917
197.
Zhou DX
Ding Xuan Zhou:
Multivariate orthonormal bases of wavelets.,
J. Math. Res. Exposition,
12
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MR1167348
198.
Zhou DX, Bi N
Ding Xuan Zhou, Ning Bi:
Wavelets and Besov spaces \(B^s_{p,q}\).,
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MR1205067
199.
Zhou DX, Xuan PC, Zhang ZQ
Ding Xuan Zhou, Pei Cai Xuan, Zhen Qiu Zhang:
Uniform approximation by multidimensional Szász-Mirakjan operators.,
J. Math. Res. Exposition,
12
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200.
Zhou DX
Ding Xuan Zhou:
Wavelet transform, Toeplitz type operators and decomposition of functions on the upper half-plane.,
Rev. Anal. Numér. Théor. Approx.,
21
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MR1202411
201.
Zhou DX
Ding Xuan Zhou:
On a conjecture of Z. Ditzian.,
J. Approx. Theory,
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MR1160252
202.
Zhou DX
Ding Xuan Zhou:
\(L^p\)-inverse theorems for beta operators.,
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MR1122289
203.
Zhou DX
Ding Xuan Zhou:
Inverse theorems in \(L^p\) for some multidimensional positive linear operators.,
Chinese Ann. Math. Ser. B,
12
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204.
Zhou DX
Ding Xuan Zhou:
A note on pseudo ideals of fields.,
J. Math. Res. Exposition,
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205.
Zhou DX
Ding Xuan Zhou:
Uniform approximation by some Durrmeyer operators.,
Approx. Theory Appl.,
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206.
Zhou DX
Ding Xuan Zhou:
Inverse theorems for some multidimensional operators.,
Approx. Theory Appl.,
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