Professor Zhou has worked on learning theory, neural networks, wavelet analysis, and approximation theory. His current research interest is theory of deep learning. He has authored more than 100 research papers.

Awards and honours

Professor Zhou has conducted more than 40 research grants as PI, supervised more than 20 PhD students, and co-organized more than 20 international conferences. He is currently serving on the editorial boards of more than ten international journals and is an editor-in-chief of the journal “Analysis and Application” of “Mathematical Foundations of Computing”.

He received a Humboldt Research Fellowship in 1993, a fund for Distinguished Young Scholars from the National Science Foundation of China in 2005 and was rated as a Highly-cited Researcher by Thomson Reuters/Clarivate Analytics in 2014-17 and World's Top 2% Scientist by Stanford University in 2021 2022, 2023.

Current research students

Project title	Research student
Operator Learning	Peilin LIU
Learning theory of deep learning and deep neural networks	Kejia TANG
Pairwise Learning with Deep Neural Networks	Junyu ZHOU

Publications

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Journals

Liu, L., Zhou, D. (2025). Analysis of regularized federated learning. Neurocomputing, 611(1 January 2025), Article 128579 - 1-Article 128579 - 12. [More Information]
Liu, P., Liu, Y., Zhou, X., Zhou, D. (2025). Approximation of functionals on Korobov spaces with Fourier Functional Networks. Neural Networks, 182, Article 106922-1-Article 106922-8. [More Information]
Liu, P., Zhou, D. (2025). Generalization Analysis of Transformers in Distribution Regression. Neural Computation, 37(2), 260-293. [More Information]
Lei, G., Lei, Z., Shi, L., Zeng, C., Zhou, D. (2025). Solving PDEs on spheres with physics-informed convolutional neural networks. Applied and Computational Harmonic Analysis, 74 Open Access, 101714 - 1-101714 - 40. [More Information]
Li, J., Feng, H., Zhou, D. (2024). Approximation analysis of CNNs from a feature extraction view. Analysis and Applications, 22(3), 635-654. [More Information]
Liu, Y., Mao, T., Zhou, D. (2024). Approximation of functions from Korobov spaces by shallow neural networks. Information Sciences, 670(June 2024), art.120573 - 1-art.120573 - 13. [More Information]
Zhang, Z., Shi, L., Zhou, D. (2024). Classification with Deep Neural Networks and Logistic Loss. Journal of Machine Learning Research, 25(125), 1-117.
Wang, P., Lei, Y., Ying, Y., Zhou, D. (2024). Differentially private stochastic gradient descent with low-noise. Neurocomputing, 585, Article 127557-1-127557-14. [More Information]
Yu, Z., Fan, J., Shi, Z., Zhou, D. (2024). Distributed Gradient Descent for Functional Learning. IEEE Transactions on Information Theory, 70(9), 6547-6571. [More Information]
Fang, Q., Shi, L., Xu, M., Zhou, D. (2024). Efficient kernel canonical correlation analysis using Nyström approximation. Inverse Problems, 40(4), Article no. 045007-27 pages. [More Information]
Yang, Y., Zhou, D. (2024). Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks. Journal of Machine Learning Research, 25, 1-35.
Yang, Y., Zhou, D. (2024). Optimal Rates of Approximation by Shallow ReLUk Neural Networks and Applications to Nonparametric Regression. Constructive Approximation, Online, 1-32. [More Information]
Li, J., Feng, H., Zhou, D. (2024). SignReLU neural network and its approximation ability. Journal of Computational and Applied Mathematics, 440(April 2024), 115551-1-11555-23. [More Information]
Lin, S., Wang, D., Zhou, D. (2024). SKETCHING WITH SPHERICAL DESIGNS FOR NOISY DATA FITTING ON SPHERES. SIAM Journal on Scientific Computing, 46(1), A313-A337. [More Information]
Mao, T., Shi, Z., Zhou, D. (2023). Approximating functions with multi-features by deep convolutional neural networks. Analysis and Applications, 21(1), 93-125. [More Information]
Song, L., Fan, J., Chen, D., Zhou, D. (2023). Approximation of Nonlinear Functionals Using Deep ReLU Networks. Journal of Fourier Analysis and Applications, 29(4), Article 50-1-23. [More Information]
Song, L., Liu, Y., Fan, J., Zhou, D. (2023). Approximation of smooth functionals using deep ReLU networks. Neural Networks, 166, 424-436. [More Information]
Yu, Z., Zhou, D. (2023). Deep learning theory of distribution regression with CNNs. Advances in Computational Mathematics, 49(4), Article no. 5-40 pages. [More Information]
Wei, L., Yu, Z., Zhou, D. (2023). Federated learning for minimizing nonsmooth convex loss functions. Mathematical Foundations of Computing, 6(4), 753-770. [More Information]
Feng, H., Huang, S., Zhou, D. (2023). Generalization Analysis of CNNs for Classification on Spheres. IEEE Transactions on Neural Networks and Learning Systems, 34(9), 6200-6213. [More Information]
Huang, S., Zhou, J., Feng, H., Zhou, D. (2023). Generalization analysis of pairwise learning for ranking with deep neural networks. Neural Computation, 35, 1135-1158. [More Information]
Feng, H., Lin, S., Zhou, D. (2023). Radial Basis Function Approximation with Distributively Stored Data on Spheres. Constructive Approximation, Published Online(April 2023), 31 pages. [More Information]
Mao, T., Zhou, D. (2023). Rates of approximation by ReLU shallow neural networks. Journal Of Complexity, 79, Article 101784-1-101784-21. [More Information]
Mao, T., Zhou, D. (2022). Approximation of functions from Korobov spaces by deep convolutional neural networks. Advances in Computational Mathematics, 48 (Open Access)(6), Article 84-26 pages. [More Information]
Feng, H., Hou, S., Wei, L., Zhou, D. (2022). CNN MODELS FOR READABILITY OF CHINESE TEXTS. Mathematical Foundations of Computing, 5(4), 351-362. [More Information]
Han, Z., Yu, S., Lin, S., Zhou, D. (2022). Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 1853-1868. [More Information]
Donoho, D., Mhaskar, H., Zhou, D. (2022). Introduction to the Special Issue on Harmonic Analysis and Machine Learning. Applied and Computational Harmonic Analysis, 59, 1-2. [More Information]
Zeng, J., Yin, W., Zhou, D. (2022). Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints. Journal of Scientific Computing, 91(2), 61-1-61-36. [More Information]
Guo, X., Lin, J., Zhou, D. (2022). Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces. Applied and Computational Harmonic Analysis, 61, 288-318. [More Information]
Chui, C., Lin, S., Zhang, B., Zhou, D. (2022). Realization of Spatial Sparseness by Deep ReLU Nets with Massive Data. IEEE Transactions on Neural Networks and Learning Systems, 33(1.0), 229-243. [More Information]
Lin, S., Wang, K., Wang, Y., Zhou, D. (2022). Universal Consistency of Deep Convolutional Neural Networks. IEEE Transactions on Information Theory, 68(7.0), 4610-4617. [More Information]
Hu, T., Zhou, D. (2021). Distributed regularized least squares with flexible Gaussian kernels. Applied and Computational Harmonic Analysis, 53, 349-377. [More Information]
Hu, T., Wu, Q., Zhou, D. (2021). Kernel gradient descent algorithm for information theoretic learning. Journal Of Approximation Theory, 263, 105518. [More Information]
Zeng, J., Lin, S., Yao, Y., Zhou, D. (2021). On ADMM in deep learning: Convergence and saturation-avoidance. Journal of Machine Learning Research, 22.
Yu, Z., Ho, D., Shi, Z., Zhou, D. (2021). Robust kernel-based distribution regression. Inverse Problems, 37(10.0), 105014. [More Information]
Mao, T., Shi, Z., Zhou, D. (2021). Theory of deep convolutional neural networks III: Approximating radial functions. Neural Networks, 144, 778-790. [More Information]
Lei, Y., Zhou, D. (2020). Convergence of online mirror descent. Applied and Computational Harmonic Analysis, 48(1.0), 343-373. [More Information]
Lin, S., Wang, Y., Zhou, D. (2020). Distributed filtered hyperinterpolation for noisy data on the sphere. SIAM Journal on Numerical Analysis, 59(2.0), 634-659. [More Information]
Hu, T., Wu, Q., Zhou, D. (2020). Distributed kernel gradient descent algorithm for minimum error entropy principle. Applied and Computational Harmonic Analysis, 49(1.0), 229-256. [More Information]
Lin, S., Wang, D., Zhou, D. (2020). Distributed kernel ridge regression with communications. Journal of Machine Learning Research, 21.
Fang, Z., Guo, Z., Zhou, D. (2020). Optimal learning rates for distribution regression. Journal Of Complexity, 56, 101426. [More Information]
Chirstmann, A., Wu, Q., Zhou, D. (2020). Preface to the special issue on analysis in machine learning and data science. Communications on Pure and Applied Analysis, 19(8), I-III. [More Information]
Fang, Z., Feng, H., Huang, S., Zhou, D. (2020). Theory of deep convolutional neural networks II: Spherical analysis. Neural Networks, 131, 154-162. [More Information]
Zhou, D. (2020). Theory of deep convolutional neural networks: Downsampling. Neural Networks, 124, 319-327. [More Information]
Zhou, D. (2020). Universality of deep convolutional neural networks. Applied and Computational Harmonic Analysis, 48(2.0), 787-794. [More Information]
Lei, Y., Zhou, D. (2019). Analysis of Singular Value Thresholding Algorithm for Matrix Completion. Journal of Fourier Analysis and Applications, 25(6.0), 2957-2972. [More Information]
Lin, S., Lei, Y., Zhou, D. (2019). Boosted kernel ridge regression: Optimal learning rates and early stopping. Journal of Machine Learning Research, 20.0.
Lei, Y., Dogan, U., Zhou, D., Kloft, M. (2019). Data-Dependent Generalization Bounds for Multi-Class Classification. IEEE Transactions on Information Theory, 65(5), 2995-3021. [More Information]
Chui, C., Lin, S., Zhou, D. (2019). Deep Net Tree Structure for Balance of Capacity and Approximation Ability. Frontiers in Applied Mathematics and Statistics, 5, 46-1-46-11. [More Information]
Chui, C., Lin, S., Zhou, D. (2019). Deep neural networks for rotation-invariance approximation and learning. Analysis and Applications, 17(5.0), 737-772. [More Information]
Lei, Y., Yang, P., Tang, K., Zhou, D. (2019). Optimal stochastic and online learning with individual iterates. Advances in Neural Information Processing Systems, 32.
Chui, C., Lin, S., Zhou, D. (2018). Construction of Neural Networks for Realization of Localized Deep Learning. Frontiers in Applied Mathematics and Statistics, 4, 14-1-14-22. [More Information]
Zhou, D. (2018). Deep distributed convolutional neural networks: Universality. Analysis and Applications, 16(6.0), 895-919. [More Information]
Lin, S., Zhou, D. (2018). Distributed Kernel-Based Gradient Descent Algorithms. Constructive Approximation, 47(2.0), 249-276. [More Information]
Lei, Y., Zhou, D. (2018). Learning theory of randomized sparse Kaczmarz method. SIAM Journal on Imaging Sciences (SIIMS), 11(1.0), 547-574. [More Information]
Lin, J., Rosasco, L., Villa, S., Zhou, D. (2018). Modified Fejér sequences and applications. Computational Optimization and Applications, 71(1.0), 95-113. [More Information]
Lin, J., Zhou, D. (2018). Online Learning Algorithms Can Converge Comparably Fast as Batch Learning. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2367-2378. [More Information]
Lin, S., Zhou, D. (2018). Optimal Learning Rates for Kernel Partial Least Squares. Journal of Fourier Analysis and Applications, 24(3.0), 908-933. [More Information]
Christmann, A., Xiang, D., Zhou, D. (2018). Total stability of kernel methods. Neurocomputing, 289, 101-118. [More Information]
Lei, Y., Zhou, D. (2017). Analysis of online composite mirror descent algorithm. Neural Computation, 29(3.0), 825-860. [More Information]
Li, B., He, B., Zhou, D. (2017). Approximation on variable exponent spaces by linear integral operators. Journal Of Approximation Theory, 223, 29-51. [More Information]
Lin, S., Guo, X., Zhou, D. (2017). Distributed learning with regularized least squares. Journal of Machine Learning Research, 18.0, 1-31.
Chang, X., Lin, S., Zhou, D. (2017). Distributed semi-supervised learning with kernel ridge regression. Journal of Machine Learning Research, 18.0, 1-22.
Guo, Z., Lin, S., Zhou, D. (2017). Learning theory of distributed spectral algorithms. Inverse Problems, 33(7.0), 74009. [More Information]
Lin, J., Lei, Y., Zhang, B., Zhou, D. (2017). Online pairwise learning algorithms with convex loss functions. Information Sciences, 406-407, 57-70. [More Information]
Guo, Z., Ying, Y., Zhou, D. (2017). Online regularized learning with pairwise loss functions. Advances in Computational Mathematics, 43(1), 127-150. [More Information]
Guo, Z., Xiang, D., Guo, X., Zhou, D. (2017). Thresholded spectral algorithms for sparse approximations. Analysis and Applications, 15(3.0), 433-455. [More Information]
Ying, Y., Zhou, D. (2017). Unregularized online learning algorithms with general loss functions. Applied and Computational Harmonic Analysis, 42(2.0), 224-244. [More Information]
Fan, J., Hu, T., Wu, Q., Zhou, D. (2016). Consistency analysis of an empirical minimum error entropy algorithm. Applied and Computational Harmonic Analysis, 41(1.0), 164-189. [More Information]
Hu, T., Wu, Q., Zhou, D. (2016). Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression. IEEE Transactions on Signal Processing, 64(24.0), 6571-6579. [More Information]
Micchelli, C., Pontil, M., Wu, Q., Zhou, D. (2016). Error bounds for learning the kernel. Analysis and Applications, 14(6.0), 849-868. [More Information]
Lin, J., Rosasco, L., Zhou, D. (2016). Iterative regularization for learning with convex loss functions. Journal of Machine Learning Research, 17.0.
Christmann, A., Zhou, D. (2016). Learning rates for the risk of kernel-based quantile regression estimators in additive models. Analysis and Applications, 14(3.0), 449-477. [More Information]
Yuan, M., Zhou, D. (2016). Minimax optimal rates of estimation in high dimensional additive models. Annals of Statistics, 44(6.0), 2564-2593. [More Information]
Christmann, A., Zhou, D. (2016). On the robustness of regularized pairwise learning methods based on kernels. Journal Of Complexity, 37, 1-33. [More Information]
Ying, Y., Zhou, D. (2016). Online pairwise learning algorithms. Neural Computation, 28(4.0), 743-777. [More Information]
Smale, S., Zhou, D. (2016). Preface. Analysis and Applications, 14(6.0), v-vi. [More Information]
Guo, X., Fan, J., Zhou, D. (2016). Sparsity and error analysis of empirical feature-based regularization schemes. Journal of Machine Learning Research, 17, 1-34.
Li, L., Zhou, D. (2015). Learning Theory Approach to a System Identification Problem Involving Atomic Norm. Journal of Fourier Analysis and Applications, 21.0 (4), 734-753. [More Information]
Lin, J., Zhou, D. (2015). Learning theory of randomized Kaczmarz algorithm. Journal of Machine Learning Research, 16, 3341-3365.
Hu, T., Wu, F., Zhou, D. (2015). Regularization schemes for minimum error entropy principle. Analysis and Applications, 13(4), 437-455. [More Information]
Li, B., Zhou, D. (2014). Analysis of approximation by linear operators on variable L ρ p (·) spaces and applications in learning theory. Abstract And Applied Analysis, 2014. [More Information]
Chen, A., Li, J., Chen, Y., Zhou, D. (2014). Asymptotic behaviour of extinction probability of interacting branching collision processes. Journal of Applied Probability, 51(1), 219-234. [More Information]
Wang, H., Xiao, Q., Zhou, D. (2013). An approximation theory approach to learning with ℓ1 regularization. Journal Of Approximation Theory, 167, 240-258. [More Information]
Guo, Z., Zhou, D. (2013). Concentration estimates for learning with unbounded sampling. Advances in Computational Mathematics, 38(1), 207-223. [More Information]
Zhou, D. (2013). Density problem and approximation error in learning theory. Abstract And Applied Analysis, 2013. [More Information]
Hu, T., Fan, J., Wu, Q., Zhou, D. (2013). Learning theory approach to minimum error entropy criterion. Journal of Machine Learning Research, 14(1), 377-397.
Zhou, D. (2013). On grouping effect of elastic net. Statistics and Probability Letters, 83(9), 2108-2112. [More Information]
Gonska, H., Prestin, J., Tachev, G., Zhou, D. (2013). Simultaneous approximation by Bernstein operators in Hölder norms. Mathematische Nachrichten, 286(4), 349-359. [More Information]
Guo, X., Zhou, D. (2012). An empirical feature-based learning algorithm producing sparse approximations. Applied and Computational Harmonic Analysis, 32(3), 389-400. [More Information]
Xiang, D., Hu, T., Zhou, D. (2012). Approximation analysis of learning algorithms for support vector regression and quantile regression. Journal of Applied Mathematics, 2012. [More Information]
Chen, A., Li, J., Chen, Y., Zhou, D. (2012). Extinction probability of interacting branching collision processes. Advances in Applied Probability, 44(1), 226-259. [More Information]
Hu, T., Xiang, D., Zhou, D. (2012). Online learning for quantile regression and support vector regression. Journal of Statistical Planning and Inference, 142(12), 3107-3122. [More Information]
Shi, L., Feng, Y., Zhou, D. (2011). Concentration estimates for learning with l1-regularizer and data dependent hypothesis spaces. Applied and Computational Harmonic Analysis, 31(2), 286-302. [More Information]
Xiang, D., Hu, T., Zhou, D. (2011). Learning with varying insensitive loss. Applied Mathematics Letters, 24(12), 2107-2109. [More Information]
Zhou, X., Shi, L., Zhou, D. (2011). Non-uniform randomized sampling for multivariate approximation by high order Parzen windows. Canadian Mathematical Bulletin, 54(3), 566-576. [More Information]
Shi, L., Zhou, D. (2011). Normal estimation on manifolds by gradient learning. Numerical Linear Algebra with Applications, 18(2), 249-259. [More Information]
Wang, C., Zhou, D. (2011). Optimal learning rates for least squares regularized regression with unbounded sampling. Journal Of Complexity, 27(1), 55-67. [More Information]
Shi, L., Guo, X., Zhou, D. (2010). Hermite learning with gradient data. Journal of Computational and Applied Mathematics, 233(11), 3046-3059. [More Information]
Xiao, Q., Zhou, D. (2010). Learning by nonsymmetric kernels with data dependent spaces and l1-regularizer. Taiwanese Journal of Mathematics, 14(5), 1821-1836. [More Information]
Mukherjee, S., Wu, Q., Zhou, D. (2010). Learning gradients on manifolds. Bernoulli, 16(1), 181-207. [More Information]
Wang, H., Xiang, D., Zhou, D. (2010). Moving least-square method in learning theory. Journal Of Approximation Theory, 162(3), 599-614. [More Information]
Hu, T., Zhou, D. (2010). Online classification with varying Gaussians. Studies in Applied Mathematics, 124(1), 65-83. [More Information]
Xiang, D., Zhou, D. (2009). Classification with gaussians and convex loss. Journal of Machine Learning Research, 10, 2447-2468.
Smale, S., Zhou, D. (2009). Geometry on probability spaces. Constructive Approximation, 30(3), 311-323. [More Information]
Cai, J., Wang, H., Zhou, D. (2009). Gradient learning in a classification setting by gradient descent. Journal Of Approximation Theory, 161(2), 674-692. [More Information]
Zhou, X., Zhou, D. (2009). High order Parzen windows and randomized sampling. Advances in Computational Mathematics, 31(4), 349-368. [More Information]
Hu, T., Zhou, D. (2009). Online learning with samples drawn from non-identical distributions. Journal of Machine Learning Research, 10, 2873-2898.
Zhou, D. (2008). Derivative reproducing properties for kernel methods in learning theory. Journal of Computational and Applied Mathematics, 220(1-2), 456-463. [More Information]
Ye, G., Zhou, D. (2008). Learning and approximation by Gaussians on Riemannian manifolds. Advances in Computational Mathematics, 29(3), 291-310. [More Information]
Dong, X., Zhou, D. (2008). Learning gradients by a gradient descent algorithm. Journal of Mathematical Analysis and Applications, 341(2), 1018-1027. [More Information]
Pan, Z., Xiang, D., Xiao, Q., Zhou, D. (2008). Parzen windows for multi-class classification. Journal Of Complexity, 24(5-6), 606-618. [More Information]
Sun, H., Zhou, D. (2008). Reproducing kernel hilbert spaces associated with analytic translation-invariant mercer kernels. Journal of Fourier Analysis and Applications, 14(1), 89-101. [More Information]
Ye, G., Zhou, D. (2007). Fully online classification by regularization. Applied and Computational Harmonic Analysis, 23(2), 198-214. [More Information]
Ying, Y., Zhou, D. (2007). Learnability of Gaussians with flexible variances. Journal of Machine Learning Research, 8, 249-276.
Smale, S., Zhou, D. (2007). Learning theory estimates via integral operators and their approximations. Constructive Approximation, 26(2), 153-172. [More Information]
Wu, Q., Ying, Y., Zhou, D. (2007). Multi-kernel regularized classifiers. Journal Of Complexity, 23(1), 108-134. [More Information]
Wu, Q., Zhou, D. (2006). Analysis of support vector machine classification. Journal of Computational Analysis and Applications, 8(2), 99-119.
Zhou, D., Jetter, K. (2006). Approximation with polynomial kernels and SVM classifiers. Advances in Computational Mathematics, 25(1-3), 323-344. [More Information]
Mukherjee, S., Zhou, D. (2006). Learning coordinate covariances via gradients. Journal of Machine Learning Research, 7, 519-549.
Wu, Q., Ying, Y., Zhou, D. (2006). Learning rates of least-square regularized regression. Foundations of Computational Mathematics, 6(2), 171-192. [More Information]
Ying, Y., Zhou, D. (2006). Online regularized classification algorithms. IEEE Transactions on Information Theory, 52(11), 4775-4788. [More Information]
Smale, S., Zhou, D. (2005). Shannon sampling II: Connections to learning theory. Applied and Computational Harmonic Analysis, 19(3), 285-302. [More Information]
Wu, Q., Zhou, D. (2005). SVM soft margin classifiers: Linear programming versus quadratic programming. Neural Computation, 17(5), 1160-1187. [More Information]
Cucker, F., Smale, S., Zhou, D. (2004). Modeling language evolution. Foundations of Computational Mathematics, 4(3), 315-343. [More Information]
Smale, S., Zhou, D. (2004). Shannon sampling and function reconstruction from point values. Bulletin of the American Mathematical Society, 41(3), 279-305. [More Information]
Chen, D., Wu, Q., Ying, Y., Zhou, D. (2004). Support vector machine soft margin classifiers: Error analysis. Journal of Machine Learning Research, 5, 1143-1175.
Zhou, D. (2003). Capacity of reproducing kernel spaces in learning theory. IEEE Transactions on Information Theory, 49(7), 1743-1752. [More Information]
Jia, R., Wang, J., Zhou, D. (2003). Compactly supported wavelet bases for Sobolev spaces. Applied and Computational Harmonic Analysis, 15(3), 224-241. [More Information]
Plonka, G., Zhou, D. (2003). Properties of locally linearly independent refinable function vectors. Journal Of Approximation Theory, 122(1), 24-41. [More Information]
Zhou, D. (2002). Interpolatory orthogonal multiwavelets and refinable functions. IEEE Transactions on Signal Processing, 50(3), 520-527. [More Information]
Nielsen, M., Zhou, D. (2002). Mean size of wavelet packets. Applied and Computational Harmonic Analysis, 13(1), 22-34. [More Information]
Cheung, H., Tang, C., Zhou, D. (2002). Supports of locally linearly independent M-refinable functions, attractors of iterated function systems and tilings. Advances in Computational Mathematics, 17(3), 257-268. [More Information]
Zhou, D. (2002). The covering number in learning theory. Journal Of Complexity, 18(3), 739-767. [More Information]
Zhou, D. (2002). Two-scale homogeneous functions in wavelet analysis. Journal of Fourier Analysis and Applications, 8(6), 565-580. [More Information]
Boyd, G., Micchelli, C., Strang, G., Zhou, D. (2001). Binomial matrices. Advances in Computational Mathematics, 14(4), 379-391. [More Information]
Zhou, D. (2001). Norms Concerning Subdivision Sequences and Their Applications in Wavelets. Applied and Computational Harmonic Analysis, 11(3), 329-346. [More Information]
Zhou, D. (2001). Self-similar lattice tilings and subdivision schemes. SIAM Journal On Mathematical Analysis, 33(1), 1-15. [More Information]
Zhou, D. (2001). Spectra of subdivision operators. Proceedings of the American Mathematical Society, 129(1), 191-202. [More Information]
Strang, G., Zhou, D. (2001). The limits of refinable functions. Transactions of the American Mathematical Society, 353(5), 1971-1984. [More Information]
Goodman, T., Jia, R., Zhou, D. (2000). Local linear independence of refinable vectors of functions. Proceedings of the Royal Society of Edinburgh Section A (Mathematics), 130(4), 813-826. [More Information]
Zhou, D. (2000). Multiple Refinable Hermite Interpolants. Journal Of Approximation Theory, 102(1), 46-71. [More Information]
Jia, R., Zhou, D. (1999). Convergence of subdivision schemes associated with nonnegative masks. SIAM Journal on Matrix Analysis and Applications, 21(2), 418-430. [More Information]
Jia, R., Riemenschneider, S., Zhou, D. (1999). Smoothness of multiple refinable functions and multiple wavelets. SIAM Journal on Matrix Analysis and Applications, 21(1), 1-28. [More Information]
Zhou, D. (1999). Solvability of linear systems of pde's with constant coefficients. Proceedings of the American Mathematical Society, 127(7), 2013-2017. [More Information]
Strang, G., Zhou, D. (1998). Inhomogeneous Refinement Equations. Journal of Fourier Analysis and Applications, 4(6), 733-747. [More Information]
Zhou, D. (1998). Some characterizations for box spline wavelets and linear diophantine equations. Rocky Mountain Journal of Mathematics, 28(4), 1539-1560. [More Information]
Jia, R., Riemenschneider, S., Zhou, D. (1998). Vector subdivision schemes and multiple wavelets. Mathematics of Computation, 67(224), 1533-1563. [More Information]
Zhou, D. (1997). Existence of multiple refinable distributions. Michigan Mathematical Journal, 44(2), 317-329. [More Information]
Zhou, D. (1997). Extendibility of rational matrices. Journal Of Approximation Theory, 88(2), 272-274. [More Information]
Zhou, D. (1996). Box splines with rational directions and linear diophantine equations. Journal of Mathematical Analysis and Applications, 203(1), 270-277. [More Information]
Mache, D., Zhou, D. (1996). Characterization theorems for the approximation by a family of operators. Journal Of Approximation Theory, 84(2), 145-161. [More Information]
Zhou, D. (1996). Linear dependence relations in wavelets and tilings. Linear Algebra and its Applications, 249(1-3), 311-323. [More Information]
Zhou, D. (1996). Stability of refinable functions, multiresolution analysis, and Haar bases. SIAM Journal On Mathematical Analysis, 27(3), 891-904. [More Information]
Mache, D., Zhou, D. (1995). Best direct and converse results for Lagrange-type operators. Analysis in Theory and Applications, 11(2), 76-93. [More Information]
Zhou, D., Jetter, K. (1995). Characterisation of Correctness of Cardinal Interpolation with Shifted Three-Directional Box Splines. Proceedings of the Royal Society of Edinburgh Section A (Mathematics), 125(5), 931-937. [More Information]
Zhou, D. (1995). Construction of Real-Valued Wavelets by Symmetry. Journal Of Approximation Theory, 81(3), 323-331. [More Information]
Guo, Z., Zhou, D. (1995). Local Approximation by Modified Szász Operators. Journal of Mathematical Analysis and Applications, 195(2), 323-334. [More Information]
Gonska, H., Zhou, D. (1995). Local smoothness of functions and Bernstein-Durrmeyer operators. Computers and Mathematics with Applications, 30(3-6), 83-101. [More Information]
Zhou, D. (1995). On Smoothness Characterized by Bernstein Type Operators. Journal Of Approximation Theory, 81(3), 303-315. [More Information]
Jetter, K., Zhou, D. (1995). Order of linear approximation from shift-invariant spaces. Constructive Approximation, 11(4), 423-438. [More Information]
Ye, M., Zhou, D. (1994). A class of operators by means of three-diagonal matrices. Journal Of Approximation Theory, 78(2), 239-259. [More Information]
Zhou, D. (1994). On wavelets in L1. Acta Mathematicae Applicatae Sinica, 10(1), 69-74. [More Information]
Zhou, D. (1994). Weighted Approximation by Multidimensional Bernstein Operators. Journal Of Approximation Theory, 76(3), 403-422. [More Information]
Zhou, D. (1994). Weighted Approximation by Szász-Mirakjan Operators. Journal Of Approximation Theory, 76(3), 393-402. [More Information]
Zhou, D. (1993). On a paper of mazhar and totik. Journal Of Approximation Theory, 72(3), 290-300. [More Information]
Zhou, D. (1992). A note on Bernstein type operators. Analysis in Theory and Applications, 8(1), 97-100. [More Information]
Zhou, D. (1992). Inverse theorems for multidimensional Bernstein-Durrmeyer operators in Lp. Journal Of Approximation Theory, 70(1), 68-93. [More Information]
Zhou, D. (1992). On a conjecture of Z. Ditzian. Journal Of Approximation Theory, 69(2), 167-172. [More Information]
Zhou, D. (1991). Lp-inverse theorems for beta operators. Journal Of Approximation Theory, 66(3), 279-287. [More Information]
Zhou, D. (1990). Inverse theorems for some multidimensional operators. Analysis in Theory and Applications, 6(4), 25-39. [More Information]
Zhou, D. (1990). Uniform approximation by some Durrmeyer operators. Analysis in Theory and Applications, 6(2), 87-100. [More Information]

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Conferences

Lei, Y., Yang, T., Ying, Y., Zhou, D. (2023). Generalization analysis for contrastive representation learning. ICML | 2023 Fortieth International Conference on Machine Learning, USA: ICML.
Zeng, J., Xie, Y., Yu, X., Lee, J., Zhou, D. (2022). Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression. (2022 Conference on Empirical Methods in Natural Language Processing, USA: Association for Computational Linguistics (ACL).
Wang, P., Lei, Y., Ying, Y., Zhou, D. (2022). Stability and generalization for Markov Chain stochastic gradient methods. NeurIPS 2022: 36th Conference on Neural Information Processing Systems, USA: Neural Information Processing Systems Foundation, Inc.
Cao, Y., Fang, Z., Wu, Y., Zhou, D., Gu, Q. (2021). Towards Understanding the Spectral Bias of Deep Learning. 30th International Joint Conference on Artificial Intelligence, IJCAI 2021. International Joint Conferences on Artificial Intelligence.
Boissier, M., Lyu, S., Ying, Y., Zhou, D. (2016). Fast convergence of online pairwise learning algorithms. The 18th International Conference on Artificial Intelligence and Statistics, Germany: AISTATS.

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By Year

Expand all

2025

Liu, L., Zhou, D. (2025). Analysis of regularized federated learning. Neurocomputing, 611(1 January 2025), Article 128579 - 1-Article 128579 - 12. [More Information]
Liu, P., Liu, Y., Zhou, X., Zhou, D. (2025). Approximation of functionals on Korobov spaces with Fourier Functional Networks. Neural Networks, 182, Article 106922-1-Article 106922-8. [More Information]
Liu, P., Zhou, D. (2025). Generalization Analysis of Transformers in Distribution Regression. Neural Computation, 37(2), 260-293. [More Information]
Lei, G., Lei, Z., Shi, L., Zeng, C., Zhou, D. (2025). Solving PDEs on spheres with physics-informed convolutional neural networks. Applied and Computational Harmonic Analysis, 74 Open Access, 101714 - 1-101714 - 40. [More Information]

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2024

Li, J., Feng, H., Zhou, D. (2024). Approximation analysis of CNNs from a feature extraction view. Analysis and Applications, 22(3), 635-654. [More Information]
Liu, Y., Mao, T., Zhou, D. (2024). Approximation of functions from Korobov spaces by shallow neural networks. Information Sciences, 670(June 2024), art.120573 - 1-art.120573 - 13. [More Information]
Zhang, Z., Shi, L., Zhou, D. (2024). Classification with Deep Neural Networks and Logistic Loss. Journal of Machine Learning Research, 25(125), 1-117.
Wang, P., Lei, Y., Ying, Y., Zhou, D. (2024). Differentially private stochastic gradient descent with low-noise. Neurocomputing, 585, Article 127557-1-127557-14. [More Information]
Yu, Z., Fan, J., Shi, Z., Zhou, D. (2024). Distributed Gradient Descent for Functional Learning. IEEE Transactions on Information Theory, 70(9), 6547-6571. [More Information]
Fang, Q., Shi, L., Xu, M., Zhou, D. (2024). Efficient kernel canonical correlation analysis using Nyström approximation. Inverse Problems, 40(4), Article no. 045007-27 pages. [More Information]
Yang, Y., Zhou, D. (2024). Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks. Journal of Machine Learning Research, 25, 1-35.
Yang, Y., Zhou, D. (2024). Optimal Rates of Approximation by Shallow ReLUk Neural Networks and Applications to Nonparametric Regression. Constructive Approximation, Online, 1-32. [More Information]
Li, J., Feng, H., Zhou, D. (2024). SignReLU neural network and its approximation ability. Journal of Computational and Applied Mathematics, 440(April 2024), 115551-1-11555-23. [More Information]
Lin, S., Wang, D., Zhou, D. (2024). SKETCHING WITH SPHERICAL DESIGNS FOR NOISY DATA FITTING ON SPHERES. SIAM Journal on Scientific Computing, 46(1), A313-A337. [More Information]

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2023

Mao, T., Shi, Z., Zhou, D. (2023). Approximating functions with multi-features by deep convolutional neural networks. Analysis and Applications, 21(1), 93-125. [More Information]
Song, L., Fan, J., Chen, D., Zhou, D. (2023). Approximation of Nonlinear Functionals Using Deep ReLU Networks. Journal of Fourier Analysis and Applications, 29(4), Article 50-1-23. [More Information]
Song, L., Liu, Y., Fan, J., Zhou, D. (2023). Approximation of smooth functionals using deep ReLU networks. Neural Networks, 166, 424-436. [More Information]
Yu, Z., Zhou, D. (2023). Deep learning theory of distribution regression with CNNs. Advances in Computational Mathematics, 49(4), Article no. 5-40 pages. [More Information]
Wei, L., Yu, Z., Zhou, D. (2023). Federated learning for minimizing nonsmooth convex loss functions. Mathematical Foundations of Computing, 6(4), 753-770. [More Information]
Lei, Y., Yang, T., Ying, Y., Zhou, D. (2023). Generalization analysis for contrastive representation learning. ICML | 2023 Fortieth International Conference on Machine Learning, USA: ICML.
Feng, H., Huang, S., Zhou, D. (2023). Generalization Analysis of CNNs for Classification on Spheres. IEEE Transactions on Neural Networks and Learning Systems, 34(9), 6200-6213. [More Information]
Huang, S., Zhou, J., Feng, H., Zhou, D. (2023). Generalization analysis of pairwise learning for ranking with deep neural networks. Neural Computation, 35, 1135-1158. [More Information]
Feng, H., Lin, S., Zhou, D. (2023). Radial Basis Function Approximation with Distributively Stored Data on Spheres. Constructive Approximation, Published Online(April 2023), 31 pages. [More Information]
Mao, T., Zhou, D. (2023). Rates of approximation by ReLU shallow neural networks. Journal Of Complexity, 79, Article 101784-1-101784-21. [More Information]

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2022

Mao, T., Zhou, D. (2022). Approximation of functions from Korobov spaces by deep convolutional neural networks. Advances in Computational Mathematics, 48 (Open Access)(6), Article 84-26 pages. [More Information]
Feng, H., Hou, S., Wei, L., Zhou, D. (2022). CNN MODELS FOR READABILITY OF CHINESE TEXTS. Mathematical Foundations of Computing, 5(4), 351-362. [More Information]
Han, Z., Yu, S., Lin, S., Zhou, D. (2022). Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 1853-1868. [More Information]
Zeng, J., Xie, Y., Yu, X., Lee, J., Zhou, D. (2022). Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression. (2022 Conference on Empirical Methods in Natural Language Processing, USA: Association for Computational Linguistics (ACL).
Donoho, D., Mhaskar, H., Zhou, D. (2022). Introduction to the Special Issue on Harmonic Analysis and Machine Learning. Applied and Computational Harmonic Analysis, 59, 1-2. [More Information]
Zeng, J., Yin, W., Zhou, D. (2022). Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints. Journal of Scientific Computing, 91(2), 61-1-61-36. [More Information]
Guo, X., Lin, J., Zhou, D. (2022). Rates of convergence of randomized Kaczmarz algorithms in Hilbert spaces. Applied and Computational Harmonic Analysis, 61, 288-318. [More Information]
Chui, C., Lin, S., Zhang, B., Zhou, D. (2022). Realization of Spatial Sparseness by Deep ReLU Nets with Massive Data. IEEE Transactions on Neural Networks and Learning Systems, 33(1.0), 229-243. [More Information]
Wang, P., Lei, Y., Ying, Y., Zhou, D. (2022). Stability and generalization for Markov Chain stochastic gradient methods. NeurIPS 2022: 36th Conference on Neural Information Processing Systems, USA: Neural Information Processing Systems Foundation, Inc.
Lin, S., Wang, K., Wang, Y., Zhou, D. (2022). Universal Consistency of Deep Convolutional Neural Networks. IEEE Transactions on Information Theory, 68(7.0), 4610-4617. [More Information]

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2021

Hu, T., Zhou, D. (2021). Distributed regularized least squares with flexible Gaussian kernels. Applied and Computational Harmonic Analysis, 53, 349-377. [More Information]
Hu, T., Wu, Q., Zhou, D. (2021). Kernel gradient descent algorithm for information theoretic learning. Journal Of Approximation Theory, 263, 105518. [More Information]
Zeng, J., Lin, S., Yao, Y., Zhou, D. (2021). On ADMM in deep learning: Convergence and saturation-avoidance. Journal of Machine Learning Research, 22.
Yu, Z., Ho, D., Shi, Z., Zhou, D. (2021). Robust kernel-based distribution regression. Inverse Problems, 37(10.0), 105014. [More Information]
Mao, T., Shi, Z., Zhou, D. (2021). Theory of deep convolutional neural networks III: Approximating radial functions. Neural Networks, 144, 778-790. [More Information]
Cao, Y., Fang, Z., Wu, Y., Zhou, D., Gu, Q. (2021). Towards Understanding the Spectral Bias of Deep Learning. 30th International Joint Conference on Artificial Intelligence, IJCAI 2021. International Joint Conferences on Artificial Intelligence.

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2020

Lei, Y., Zhou, D. (2020). Convergence of online mirror descent. Applied and Computational Harmonic Analysis, 48(1.0), 343-373. [More Information]
Lin, S., Wang, Y., Zhou, D. (2020). Distributed filtered hyperinterpolation for noisy data on the sphere. SIAM Journal on Numerical Analysis, 59(2.0), 634-659. [More Information]
Hu, T., Wu, Q., Zhou, D. (2020). Distributed kernel gradient descent algorithm for minimum error entropy principle. Applied and Computational Harmonic Analysis, 49(1.0), 229-256. [More Information]
Lin, S., Wang, D., Zhou, D. (2020). Distributed kernel ridge regression with communications. Journal of Machine Learning Research, 21.
Fang, Z., Guo, Z., Zhou, D. (2020). Optimal learning rates for distribution regression. Journal Of Complexity, 56, 101426. [More Information]
Chirstmann, A., Wu, Q., Zhou, D. (2020). Preface to the special issue on analysis in machine learning and data science. Communications on Pure and Applied Analysis, 19(8), I-III. [More Information]
Fang, Z., Feng, H., Huang, S., Zhou, D. (2020). Theory of deep convolutional neural networks II: Spherical analysis. Neural Networks, 131, 154-162. [More Information]
Zhou, D. (2020). Theory of deep convolutional neural networks: Downsampling. Neural Networks, 124, 319-327. [More Information]
Zhou, D. (2020). Universality of deep convolutional neural networks. Applied and Computational Harmonic Analysis, 48(2.0), 787-794. [More Information]

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2019

Lei, Y., Zhou, D. (2019). Analysis of Singular Value Thresholding Algorithm for Matrix Completion. Journal of Fourier Analysis and Applications, 25(6.0), 2957-2972. [More Information]
Lin, S., Lei, Y., Zhou, D. (2019). Boosted kernel ridge regression: Optimal learning rates and early stopping. Journal of Machine Learning Research, 20.0.
Lei, Y., Dogan, U., Zhou, D., Kloft, M. (2019). Data-Dependent Generalization Bounds for Multi-Class Classification. IEEE Transactions on Information Theory, 65(5), 2995-3021. [More Information]
Chui, C., Lin, S., Zhou, D. (2019). Deep Net Tree Structure for Balance of Capacity and Approximation Ability. Frontiers in Applied Mathematics and Statistics, 5, 46-1-46-11. [More Information]
Chui, C., Lin, S., Zhou, D. (2019). Deep neural networks for rotation-invariance approximation and learning. Analysis and Applications, 17(5.0), 737-772. [More Information]
Lei, Y., Yang, P., Tang, K., Zhou, D. (2019). Optimal stochastic and online learning with individual iterates. Advances in Neural Information Processing Systems, 32.

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2018

Chui, C., Lin, S., Zhou, D. (2018). Construction of Neural Networks for Realization of Localized Deep Learning. Frontiers in Applied Mathematics and Statistics, 4, 14-1-14-22. [More Information]
Zhou, D. (2018). Deep distributed convolutional neural networks: Universality. Analysis and Applications, 16(6.0), 895-919. [More Information]
Lin, S., Zhou, D. (2018). Distributed Kernel-Based Gradient Descent Algorithms. Constructive Approximation, 47(2.0), 249-276. [More Information]
Lei, Y., Zhou, D. (2018). Learning theory of randomized sparse Kaczmarz method. SIAM Journal on Imaging Sciences (SIIMS), 11(1.0), 547-574. [More Information]
Lin, J., Rosasco, L., Villa, S., Zhou, D. (2018). Modified Fejér sequences and applications. Computational Optimization and Applications, 71(1.0), 95-113. [More Information]
Lin, J., Zhou, D. (2018). Online Learning Algorithms Can Converge Comparably Fast as Batch Learning. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2367-2378. [More Information]
Lin, S., Zhou, D. (2018). Optimal Learning Rates for Kernel Partial Least Squares. Journal of Fourier Analysis and Applications, 24(3.0), 908-933. [More Information]
Christmann, A., Xiang, D., Zhou, D. (2018). Total stability of kernel methods. Neurocomputing, 289, 101-118. [More Information]

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2017

Lei, Y., Zhou, D. (2017). Analysis of online composite mirror descent algorithm. Neural Computation, 29(3.0), 825-860. [More Information]
Li, B., He, B., Zhou, D. (2017). Approximation on variable exponent spaces by linear integral operators. Journal Of Approximation Theory, 223, 29-51. [More Information]
Lin, S., Guo, X., Zhou, D. (2017). Distributed learning with regularized least squares. Journal of Machine Learning Research, 18.0, 1-31.
Chang, X., Lin, S., Zhou, D. (2017). Distributed semi-supervised learning with kernel ridge regression. Journal of Machine Learning Research, 18.0, 1-22.
Guo, Z., Lin, S., Zhou, D. (2017). Learning theory of distributed spectral algorithms. Inverse Problems, 33(7.0), 74009. [More Information]
Lin, J., Lei, Y., Zhang, B., Zhou, D. (2017). Online pairwise learning algorithms with convex loss functions. Information Sciences, 406-407, 57-70. [More Information]
Guo, Z., Ying, Y., Zhou, D. (2017). Online regularized learning with pairwise loss functions. Advances in Computational Mathematics, 43(1), 127-150. [More Information]
Guo, Z., Xiang, D., Guo, X., Zhou, D. (2017). Thresholded spectral algorithms for sparse approximations. Analysis and Applications, 15(3.0), 433-455. [More Information]
Ying, Y., Zhou, D. (2017). Unregularized online learning algorithms with general loss functions. Applied and Computational Harmonic Analysis, 42(2.0), 224-244. [More Information]

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2016

Fan, J., Hu, T., Wu, Q., Zhou, D. (2016). Consistency analysis of an empirical minimum error entropy algorithm. Applied and Computational Harmonic Analysis, 41(1.0), 164-189. [More Information]
Hu, T., Wu, Q., Zhou, D. (2016). Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression. IEEE Transactions on Signal Processing, 64(24.0), 6571-6579. [More Information]
Micchelli, C., Pontil, M., Wu, Q., Zhou, D. (2016). Error bounds for learning the kernel. Analysis and Applications, 14(6.0), 849-868. [More Information]
Boissier, M., Lyu, S., Ying, Y., Zhou, D. (2016). Fast convergence of online pairwise learning algorithms. The 18th International Conference on Artificial Intelligence and Statistics, Germany: AISTATS.
Lin, J., Rosasco, L., Zhou, D. (2016). Iterative regularization for learning with convex loss functions. Journal of Machine Learning Research, 17.0.
Christmann, A., Zhou, D. (2016). Learning rates for the risk of kernel-based quantile regression estimators in additive models. Analysis and Applications, 14(3.0), 449-477. [More Information]
Yuan, M., Zhou, D. (2016). Minimax optimal rates of estimation in high dimensional additive models. Annals of Statistics, 44(6.0), 2564-2593. [More Information]
Christmann, A., Zhou, D. (2016). On the robustness of regularized pairwise learning methods based on kernels. Journal Of Complexity, 37, 1-33. [More Information]
Ying, Y., Zhou, D. (2016). Online pairwise learning algorithms. Neural Computation, 28(4.0), 743-777. [More Information]
Smale, S., Zhou, D. (2016). Preface. Analysis and Applications, 14(6.0), v-vi. [More Information]
Guo, X., Fan, J., Zhou, D. (2016). Sparsity and error analysis of empirical feature-based regularization schemes. Journal of Machine Learning Research, 17, 1-34.

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2015

Li, L., Zhou, D. (2015). Learning Theory Approach to a System Identification Problem Involving Atomic Norm. Journal of Fourier Analysis and Applications, 21.0 (4), 734-753. [More Information]
Lin, J., Zhou, D. (2015). Learning theory of randomized Kaczmarz algorithm. Journal of Machine Learning Research, 16, 3341-3365.
Hu, T., Wu, F., Zhou, D. (2015). Regularization schemes for minimum error entropy principle. Analysis and Applications, 13(4), 437-455. [More Information]

2014

Li, B., Zhou, D. (2014). Analysis of approximation by linear operators on variable L ρ p (·) spaces and applications in learning theory. Abstract And Applied Analysis, 2014. [More Information]
Chen, A., Li, J., Chen, Y., Zhou, D. (2014). Asymptotic behaviour of extinction probability of interacting branching collision processes. Journal of Applied Probability, 51(1), 219-234. [More Information]

2013

Wang, H., Xiao, Q., Zhou, D. (2013). An approximation theory approach to learning with ℓ1 regularization. Journal Of Approximation Theory, 167, 240-258. [More Information]
Guo, Z., Zhou, D. (2013). Concentration estimates for learning with unbounded sampling. Advances in Computational Mathematics, 38(1), 207-223. [More Information]
Zhou, D. (2013). Density problem and approximation error in learning theory. Abstract And Applied Analysis, 2013. [More Information]
Hu, T., Fan, J., Wu, Q., Zhou, D. (2013). Learning theory approach to minimum error entropy criterion. Journal of Machine Learning Research, 14(1), 377-397.
Zhou, D. (2013). On grouping effect of elastic net. Statistics and Probability Letters, 83(9), 2108-2112. [More Information]
Gonska, H., Prestin, J., Tachev, G., Zhou, D. (2013). Simultaneous approximation by Bernstein operators in Hölder norms. Mathematische Nachrichten, 286(4), 349-359. [More Information]

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2012

Guo, X., Zhou, D. (2012). An empirical feature-based learning algorithm producing sparse approximations. Applied and Computational Harmonic Analysis, 32(3), 389-400. [More Information]
Xiang, D., Hu, T., Zhou, D. (2012). Approximation analysis of learning algorithms for support vector regression and quantile regression. Journal of Applied Mathematics, 2012. [More Information]
Chen, A., Li, J., Chen, Y., Zhou, D. (2012). Extinction probability of interacting branching collision processes. Advances in Applied Probability, 44(1), 226-259. [More Information]
Hu, T., Xiang, D., Zhou, D. (2012). Online learning for quantile regression and support vector regression. Journal of Statistical Planning and Inference, 142(12), 3107-3122. [More Information]

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2011

Shi, L., Feng, Y., Zhou, D. (2011). Concentration estimates for learning with l1-regularizer and data dependent hypothesis spaces. Applied and Computational Harmonic Analysis, 31(2), 286-302. [More Information]
Xiang, D., Hu, T., Zhou, D. (2011). Learning with varying insensitive loss. Applied Mathematics Letters, 24(12), 2107-2109. [More Information]
Zhou, X., Shi, L., Zhou, D. (2011). Non-uniform randomized sampling for multivariate approximation by high order Parzen windows. Canadian Mathematical Bulletin, 54(3), 566-576. [More Information]
Shi, L., Zhou, D. (2011). Normal estimation on manifolds by gradient learning. Numerical Linear Algebra with Applications, 18(2), 249-259. [More Information]
Wang, C., Zhou, D. (2011). Optimal learning rates for least squares regularized regression with unbounded sampling. Journal Of Complexity, 27(1), 55-67. [More Information]

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2010

Shi, L., Guo, X., Zhou, D. (2010). Hermite learning with gradient data. Journal of Computational and Applied Mathematics, 233(11), 3046-3059. [More Information]
Xiao, Q., Zhou, D. (2010). Learning by nonsymmetric kernels with data dependent spaces and l1-regularizer. Taiwanese Journal of Mathematics, 14(5), 1821-1836. [More Information]
Mukherjee, S., Wu, Q., Zhou, D. (2010). Learning gradients on manifolds. Bernoulli, 16(1), 181-207. [More Information]
Wang, H., Xiang, D., Zhou, D. (2010). Moving least-square method in learning theory. Journal Of Approximation Theory, 162(3), 599-614. [More Information]
Hu, T., Zhou, D. (2010). Online classification with varying Gaussians. Studies in Applied Mathematics, 124(1), 65-83. [More Information]

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2009

Xiang, D., Zhou, D. (2009). Classification with gaussians and convex loss. Journal of Machine Learning Research, 10, 2447-2468.
Smale, S., Zhou, D. (2009). Geometry on probability spaces. Constructive Approximation, 30(3), 311-323. [More Information]
Cai, J., Wang, H., Zhou, D. (2009). Gradient learning in a classification setting by gradient descent. Journal Of Approximation Theory, 161(2), 674-692. [More Information]
Zhou, X., Zhou, D. (2009). High order Parzen windows and randomized sampling. Advances in Computational Mathematics, 31(4), 349-368. [More Information]
Hu, T., Zhou, D. (2009). Online learning with samples drawn from non-identical distributions. Journal of Machine Learning Research, 10, 2873-2898.

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2008

Zhou, D. (2008). Derivative reproducing properties for kernel methods in learning theory. Journal of Computational and Applied Mathematics, 220(1-2), 456-463. [More Information]
Ye, G., Zhou, D. (2008). Learning and approximation by Gaussians on Riemannian manifolds. Advances in Computational Mathematics, 29(3), 291-310. [More Information]
Dong, X., Zhou, D. (2008). Learning gradients by a gradient descent algorithm. Journal of Mathematical Analysis and Applications, 341(2), 1018-1027. [More Information]
Pan, Z., Xiang, D., Xiao, Q., Zhou, D. (2008). Parzen windows for multi-class classification. Journal Of Complexity, 24(5-6), 606-618. [More Information]
Sun, H., Zhou, D. (2008). Reproducing kernel hilbert spaces associated with analytic translation-invariant mercer kernels. Journal of Fourier Analysis and Applications, 14(1), 89-101. [More Information]

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2007

Ye, G., Zhou, D. (2007). Fully online classification by regularization. Applied and Computational Harmonic Analysis, 23(2), 198-214. [More Information]
Ying, Y., Zhou, D. (2007). Learnability of Gaussians with flexible variances. Journal of Machine Learning Research, 8, 249-276.
Smale, S., Zhou, D. (2007). Learning theory estimates via integral operators and their approximations. Constructive Approximation, 26(2), 153-172. [More Information]
Wu, Q., Ying, Y., Zhou, D. (2007). Multi-kernel regularized classifiers. Journal Of Complexity, 23(1), 108-134. [More Information]

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2006

Wu, Q., Zhou, D. (2006). Analysis of support vector machine classification. Journal of Computational Analysis and Applications, 8(2), 99-119.
Zhou, D., Jetter, K. (2006). Approximation with polynomial kernels and SVM classifiers. Advances in Computational Mathematics, 25(1-3), 323-344. [More Information]
Mukherjee, S., Zhou, D. (2006). Learning coordinate covariances via gradients. Journal of Machine Learning Research, 7, 519-549.
Wu, Q., Ying, Y., Zhou, D. (2006). Learning rates of least-square regularized regression. Foundations of Computational Mathematics, 6(2), 171-192. [More Information]
Ying, Y., Zhou, D. (2006). Online regularized classification algorithms. IEEE Transactions on Information Theory, 52(11), 4775-4788. [More Information]

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2005

Smale, S., Zhou, D. (2005). Shannon sampling II: Connections to learning theory. Applied and Computational Harmonic Analysis, 19(3), 285-302. [More Information]
Wu, Q., Zhou, D. (2005). SVM soft margin classifiers: Linear programming versus quadratic programming. Neural Computation, 17(5), 1160-1187. [More Information]

2004

Cucker, F., Smale, S., Zhou, D. (2004). Modeling language evolution. Foundations of Computational Mathematics, 4(3), 315-343. [More Information]
Smale, S., Zhou, D. (2004). Shannon sampling and function reconstruction from point values. Bulletin of the American Mathematical Society, 41(3), 279-305. [More Information]
Chen, D., Wu, Q., Ying, Y., Zhou, D. (2004). Support vector machine soft margin classifiers: Error analysis. Journal of Machine Learning Research, 5, 1143-1175.

2003

Zhou, D. (2003). Capacity of reproducing kernel spaces in learning theory. IEEE Transactions on Information Theory, 49(7), 1743-1752. [More Information]
Jia, R., Wang, J., Zhou, D. (2003). Compactly supported wavelet bases for Sobolev spaces. Applied and Computational Harmonic Analysis, 15(3), 224-241. [More Information]
Plonka, G., Zhou, D. (2003). Properties of locally linearly independent refinable function vectors. Journal Of Approximation Theory, 122(1), 24-41. [More Information]

2002

Zhou, D. (2002). Interpolatory orthogonal multiwavelets and refinable functions. IEEE Transactions on Signal Processing, 50(3), 520-527. [More Information]
Nielsen, M., Zhou, D. (2002). Mean size of wavelet packets. Applied and Computational Harmonic Analysis, 13(1), 22-34. [More Information]
Cheung, H., Tang, C., Zhou, D. (2002). Supports of locally linearly independent M-refinable functions, attractors of iterated function systems and tilings. Advances in Computational Mathematics, 17(3), 257-268. [More Information]
Zhou, D. (2002). The covering number in learning theory. Journal Of Complexity, 18(3), 739-767. [More Information]
Zhou, D. (2002). Two-scale homogeneous functions in wavelet analysis. Journal of Fourier Analysis and Applications, 8(6), 565-580. [More Information]

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2001

Boyd, G., Micchelli, C., Strang, G., Zhou, D. (2001). Binomial matrices. Advances in Computational Mathematics, 14(4), 379-391. [More Information]
Zhou, D. (2001). Norms Concerning Subdivision Sequences and Their Applications in Wavelets. Applied and Computational Harmonic Analysis, 11(3), 329-346. [More Information]
Zhou, D. (2001). Self-similar lattice tilings and subdivision schemes. SIAM Journal On Mathematical Analysis, 33(1), 1-15. [More Information]
Zhou, D. (2001). Spectra of subdivision operators. Proceedings of the American Mathematical Society, 129(1), 191-202. [More Information]
Strang, G., Zhou, D. (2001). The limits of refinable functions. Transactions of the American Mathematical Society, 353(5), 1971-1984. [More Information]

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2000

Goodman, T., Jia, R., Zhou, D. (2000). Local linear independence of refinable vectors of functions. Proceedings of the Royal Society of Edinburgh Section A (Mathematics), 130(4), 813-826. [More Information]
Zhou, D. (2000). Multiple Refinable Hermite Interpolants. Journal Of Approximation Theory, 102(1), 46-71. [More Information]

1999

Jia, R., Zhou, D. (1999). Convergence of subdivision schemes associated with nonnegative masks. SIAM Journal on Matrix Analysis and Applications, 21(2), 418-430. [More Information]
Jia, R., Riemenschneider, S., Zhou, D. (1999). Smoothness of multiple refinable functions and multiple wavelets. SIAM Journal on Matrix Analysis and Applications, 21(1), 1-28. [More Information]
Zhou, D. (1999). Solvability of linear systems of pde's with constant coefficients. Proceedings of the American Mathematical Society, 127(7), 2013-2017. [More Information]

1998

Strang, G., Zhou, D. (1998). Inhomogeneous Refinement Equations. Journal of Fourier Analysis and Applications, 4(6), 733-747. [More Information]
Zhou, D. (1998). Some characterizations for box spline wavelets and linear diophantine equations. Rocky Mountain Journal of Mathematics, 28(4), 1539-1560. [More Information]
Jia, R., Riemenschneider, S., Zhou, D. (1998). Vector subdivision schemes and multiple wavelets. Mathematics of Computation, 67(224), 1533-1563. [More Information]

1997

Zhou, D. (1997). Existence of multiple refinable distributions. Michigan Mathematical Journal, 44(2), 317-329. [More Information]
Zhou, D. (1997). Extendibility of rational matrices. Journal Of Approximation Theory, 88(2), 272-274. [More Information]

1996

Zhou, D. (1996). Box splines with rational directions and linear diophantine equations. Journal of Mathematical Analysis and Applications, 203(1), 270-277. [More Information]
Mache, D., Zhou, D. (1996). Characterization theorems for the approximation by a family of operators. Journal Of Approximation Theory, 84(2), 145-161. [More Information]
Zhou, D. (1996). Linear dependence relations in wavelets and tilings. Linear Algebra and its Applications, 249(1-3), 311-323. [More Information]
Zhou, D. (1996). Stability of refinable functions, multiresolution analysis, and Haar bases. SIAM Journal On Mathematical Analysis, 27(3), 891-904. [More Information]

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1995

Mache, D., Zhou, D. (1995). Best direct and converse results for Lagrange-type operators. Analysis in Theory and Applications, 11(2), 76-93. [More Information]
Zhou, D., Jetter, K. (1995). Characterisation of Correctness of Cardinal Interpolation with Shifted Three-Directional Box Splines. Proceedings of the Royal Society of Edinburgh Section A (Mathematics), 125(5), 931-937. [More Information]
Zhou, D. (1995). Construction of Real-Valued Wavelets by Symmetry. Journal Of Approximation Theory, 81(3), 323-331. [More Information]
Guo, Z., Zhou, D. (1995). Local Approximation by Modified Szász Operators. Journal of Mathematical Analysis and Applications, 195(2), 323-334. [More Information]
Gonska, H., Zhou, D. (1995). Local smoothness of functions and Bernstein-Durrmeyer operators. Computers and Mathematics with Applications, 30(3-6), 83-101. [More Information]
Zhou, D. (1995). On Smoothness Characterized by Bernstein Type Operators. Journal Of Approximation Theory, 81(3), 303-315. [More Information]
Jetter, K., Zhou, D. (1995). Order of linear approximation from shift-invariant spaces. Constructive Approximation, 11(4), 423-438. [More Information]

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1994

Ye, M., Zhou, D. (1994). A class of operators by means of three-diagonal matrices. Journal Of Approximation Theory, 78(2), 239-259. [More Information]
Zhou, D. (1994). On wavelets in L1. Acta Mathematicae Applicatae Sinica, 10(1), 69-74. [More Information]
Zhou, D. (1994). Weighted Approximation by Multidimensional Bernstein Operators. Journal Of Approximation Theory, 76(3), 403-422. [More Information]
Zhou, D. (1994). Weighted Approximation by Szász-Mirakjan Operators. Journal Of Approximation Theory, 76(3), 393-402. [More Information]

show 1 more

1993

Zhou, D. (1993). On a paper of mazhar and totik. Journal Of Approximation Theory, 72(3), 290-300. [More Information]

1992

Zhou, D. (1992). A note on Bernstein type operators. Analysis in Theory and Applications, 8(1), 97-100. [More Information]
Zhou, D. (1992). Inverse theorems for multidimensional Bernstein-Durrmeyer operators in Lp. Journal Of Approximation Theory, 70(1), 68-93. [More Information]
Zhou, D. (1992). On a conjecture of Z. Ditzian. Journal Of Approximation Theory, 69(2), 167-172. [More Information]

1991

Zhou, D. (1991). Lp-inverse theorems for beta operators. Journal Of Approximation Theory, 66(3), 279-287. [More Information]

1990

Zhou, D. (1990). Inverse theorems for some multidimensional operators. Analysis in Theory and Applications, 6(4), 25-39. [More Information]
Zhou, D. (1990). Uniform approximation by some Durrmeyer operators. Analysis in Theory and Applications, 6(2), 87-100. [More Information]

Selected Grants

2024

Approximation theory of structured neural networks, Zhou D, Australian Research Council (ARC)/Discovery Projects (DP)

2022

Machine Learning Theory and Applications, Zhou D, Faculty of Science/Faculty Startup Scheme

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Professor Dingxuan Zhou

Timetable

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Selected Grants

2024

2022