Z. Han, S. Q. Yu, S. B. Lin,
and D. X. Zhou, Depth selection for deep ReLU nets in feature extraction and
generalization, IEEE Trans. Pattern Anal. Machine Intelligence {\bf 44}: 4
(2022), 1853--1868.
2. C. K. Chui, S. B. Lin, B.
Zhang, and D. X. Zhou, Realization of spatial sparseness by deep ReLU nets with
massive data, IEEE Transactions on Neural Networks and Learning Systems {\bf
33}:1 (2022), 229--243.
3. J. S. Zeng, W. Yin, and D. X.
Zhou, Moreau envelope augmented Lagrangian method for
4. nonconvex optimization with
linear constraints, Journal of Scientific Computing (2022) {\bf 91}:61.
5. DOI: 10.1007/s10915-022-01815-w
6. X. Guo, J. H. Lin, and D. X.
Zhou, Convergence of the randomized Kaczmarz algorithm in Hilbert spaces, Appl.
Comput. Harmonic Anal. {\bf 61} (2022), 288--318.
7. S. B. Lin, K. D. Wang, Y. Wang,
and D. X. Zhou, Universal consistency of deep convolutional neural networks,
IEEE Trans. Inform. Theory {\bf 68}:7 (2022), 4610--4617.
8. H. Feng, S. Z. Hou, L. Y. Wei,
and D. X. Zhou, CNN models for readability of Chinese texts, Mathematical
Foundations of Computing {\bf 5} (2022), 351--362.
9. P. Y. Wang, Y. W. Lei, Y. Ying,
and D. X. Zhou, Stability and generalization for Markov Chain stochastic
gradient methods, NeurIPS 2022.
10. J. S. Zeng, Y. D. Xie, X. L.
Yu, J. Lee, and D. X. Zhou, Enhancing automatic readability assessment with
pre-training and soft labels for ordinal regression, Findings of the
Association for Computational Linguistics: EMNLP 2022, pp. 4557–4568. (2022
Conference on Empirical Methods in Natural Language Processing).
11. T. Mao and D. X. Zhou,
Approximation of functions from Korobov spaces by deep convolutional neural
networks, Advances in Computational Mathematics (2022) 48:84
https://doi.org/10.1007/s10444-022-09991-x
12. T. Mao and D. X. Zhou, Rates of
Approximation by ReLU Shallow Neural Networks, Journal of Complexity {\bf 79}
(2023), 101784.
13. L. H. Song, Y. Liu, J. Fan, and
D. X. Zhou, Approximation of continuous and smooth functionals using deep ReLU
networks, Neural Networks {\bf 166} (2023), 424--436.
14. S. Huang, J. Y. Zhou, H. Feng,
and D. X. Zhou, Generalization analysis of pairwise learning for ranking with
deep neural networks, Neural Computation {\bf 35}:6 (2023), 1135--1158.
15. T. Mao, Z. J. Shi, and D. X.
Zhou, Approximating functions with multi-features by deep convolutional neural
networks, Anal. Appl. {\bf 21} (2023), 93--125.
16. Y. W. Lei, T. B. Yang, Y. Ying,
and D. X. Zhou, Generalization analysis for contrastive representation
learning, ICML, 2023.
17. L. H. Song, J. Fan, D. R. Chen,
and D. X. Zhou, Approximation of nonlinear functionals using deep ReLU
networks, J. Fourier Anal. Appl. (2023) 29:50
https://doi.org/10.1007/s00041-023-10027-1
18. Z. Yu and D. X. Zhou, Deep
learning theory of distribution regression with CNNs, Adv. Comput. Math. (2023)
49:51 https://doi.org/10.1007/s10444-023-10054-y
19. L. Y. Wei, Z. Yu, and D. X.
Zhou, Federated learning for minimizing nonsmooth convex loss functions,
Mathematical Foundations of Computing {\bf 6}:4 (2023), 753--770.
20. H. Feng, S. Huang, and D. X.
Zhou, Generalization analysis of CNNs for classification on spheres, IEEE
Transactions on Neural Networks and Learning Systems {\bf 34}:9 (2023),
6200--6213.
21. S. B. Lin, D. Wang, and D. X.
Zhou, Sketching with spherical designs for noisy data fitting on spheres, SIAM
Journal on Scientific Computing {\bf 46}:1 (2024), 313--337.
22. Q. Fang, L. Shi, M. Xu, and D.
X. Zhou, Efficient kernel canonical correlation analysis using Nystr\"{o}m
approximation, Inverse Problems {\bf 40} (2024), 045007 (26pp)
23. J. F. Li, H. Feng, and D. X.
Zhou, DLU neural networks and their
24. approximation power, Journal of
Computational and Applied Mathematics {\bf 440} (2024) 115551.
25. P. Y. Wang, Y. W. Lei, Y. Ying,
and D. X. Zhou, Differentially private stochastic gradient descent with
low-noise, Neurocomputing {\bf 585} (2024), 127557.
26. Y. Q. Liu, T. Mao, and D. X.
Zhou, Approximation of functions from Korobov spaces by shallow neural
networks, Information Sciences {\bf 670} (2024), 120573.
27. J. F. Li, H. Feng, and D. X.
Zhou, Approximation analysis of CNNs from a feature extraction view, Analysis
and Applications {\bf 22} (2024), 635--654.
28. Z. H. Zhang, L. Shi, and D. X.
Zhou, Classification with deep neural networks and logistic loss, Journal of
Machine Learning Research {\bf 25}(125):1−117, 2024.
29. Y. F. Yang and D. X. Zhou,
Nonparametric regression using over-parameterized shallow ReLU neural networks,
Journal of Machine Learning Research {\bf 25}(165):1−35, 2024.
30. Z. Yu, J. Fan, Z. J. Shi, and
D. X. Zhou, Distributed gradient descent for functional learning, IEEE
Transactions on Information Theory {\bf 70}:9(2024), 6547--6571.
31. X. Han, D. X. Zhou, G. J. Shen,
X. J. Kong, and Y. L. Zhao, Deep trajectory recovery approach of offline
vehicles in the internet of vehicles, IEEE Transactions on Vehicular Technology
{\bf 73}:11 (2024), 16051--16062.
32. Han Feng, Shao-Bo Lin, and
Ding-Xuan Zhou, Radial basis function approximation with distributively stored
data on spheres, Constr. Approx. {\bf 60} (2024), 1--31.
33. G. H. Lei, Z. Lei, L. Shi, C.
Y. Zeng, and D. X. Zhou, Solving PDEs on spheres with physics-informed
convolutional neural networks, Appl. Comput. Harmonic Anal. {\bf 74} (2025),
101714.
34. L. M. Liu and D. X. Zhou,
Analysis of regularized federated learning, Neurocomputing {\bf 611} (2025),
128579.
35. P. L. Liu, Y. Q. Liu, X. Zhou,
and D. X. Zhou, Approximation of functionals on Korobov spaces with Fourier
functional networks, Neural Networks {\bf 182} (2025), 106922.
36. P. L. Liu and D. X. Zhou,
Generalization analysis of transformers in distribution regression, Neural
Computation {\bf 37} (2025), 260--293.
37. P. Y. Wang, Y. W. Lei, D. Wang,
Y. Ying, D. X. Zhou, Generalization guarantees of gradient descent for shallow
neural networks, Neural Computation {\bf 37} (2025), 344--402.