Prof. Ding-Xuan ZHOU (周定軒)

Education

Ph.D. (1991) and B.Sc. (1988) in Applied Mathematics, Zhejiang University

Academic Awards

World's Top 2% Scientist, rated by Stanford University, 2021
Highly-cited Researcher, Thomson Reuters, 2014, 2015; Clarivate Analytics 2016, 2017
National Science Fund for Distinguished Young Scholars, NSF China, 2005
Humboldt Research Fellowship, Alexander von Humboldt Foundation, Germany, 1993

Recruitment

I am looking for highly motivated graduate students and postdocs interested in Learning Theory and Machine Learning with applications. Candidates with strong mathematical or computing skills are welcome to apply (mazhou@cityu.edu.hk). Please contact me if you have any inquiry.

Publications

In press:

• Z. Han, S. Q. Yu, S. B. Lin, and D. X. Zhou, Depth selection for deep ReLU nets in feature extraction and generalization, IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.
• 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, in press.

2021:

• S. B. Lin, Y. G. Wang, and D. X. Zhou, Distributed filtered hyperinterpolation for noisy data on the sphere, SIAM J. Numer. Anal. 59 (2021), 634--659.
• T. Hu and D. X. Zhou, Distributed regularized least squares with flexible Gaussian kernels, Appl. Comput. Harmonic Anal. 53 (2021), 349--377.
• T. Hu, Q. Wu and D. X. Zhou, Kernel gradient descent algorithm for information theoretic learning, J. Approx. Theory 263 (2021), 105518.
• Y. Cao, Z. Y. Fang, Y. Wu, D. X. Zhou, and Q. Q. Gu, Towards understanding the spectral bias of deep learning, IJCAI 2021.

2020:

• D. X. Zhou, Universality of deep convolutional neural networks, Applied and Computational Harmonic Analysis 48 (2020), 787-794.
• D. X. Zhou, Theory of deep convolutional neural networks: Downsampling, Neural Networks 124 (2020), 319-327.
• T. Hu, Q. Wu and D. X. Zhou, Distributed kernel gradient descent algorithm for minimum error entropy principle, Applied and Computational Harmonic Analysis 49 (2020), 229-256.
• Y. W. Lei and D. X. Zhou, Convergence of online mirror descent, Applied and Computational Harmonic Analysis 48 (2020), 343-373.
• Z. Y. Fang, Z. C. Guo, and D. X. Zhou, Optimal learning rates for distribution regression, Journal of Complexity 56 (2020), 101426.
• S. B. Lin, D. Wang, and D. X. Zhou, Distributed kernel ridge regression with communications, Journal of Machine Learning Research 21 (2020), 1-38.
• Z. Y. Fang, H. Feng, S. Huang, and D. X. Zhou, Theory of deep convolutional neural networks II: Spherical analysis, Neural Networks 131 (2020), 154-162.

2019:

• C. K. Chui, S. B. Lin, and D. X. Zhou, Deep neural networks for rotation-invariance approximation and learning, Analysis and Applications 17 (2019), 737-772.
• Y. W. Lei, U. Dogan, D. X. Zhou, and M. Kloft, Data-dependent generalization bounds for multi-class classification, IEEE Transactions on Information Theory 65 (2019), 2995-3021.
• S. B. Lin, Y. W. Lei, and D. X. Zhou, Boosted kernel ridge regression: optimal learning rates and early stopping, Journal of Machine Learning Research 20 (46):1-36, 2019.
• Y. W. Lei, P. Yang, K. Tang, and D. X. Zhou, Optimal stochastic and online learning with individual iterates, NeurIPS 2019.
• C. K. Chui, S. B. Lin, and D. X. Zhou, Deep net tree structure for balance of capacity and approximation ability, Frontiers in Applied Mathematics and Statistics 5:46 (2019). doi: 10.3389/fams.2019.00046
• Y. W. Lei and D. X. Zhou, Analysis of Singular Value Thresholding Algorithm for Matrix Completion, Journal of Fourier Analysis and Applications 25 (2019), 2957-2972.

2018:

• J. H. Lin, L. Rosasco, S. Villa, and D. X. Zhou, Modified Fejér sequences and applications, Computational Optimization and Applications 71 (2018), 95-113.
• D. X. Zhou, Deep distributed convolutional neural networks: Universality, Analysis and Applications 16 (2018), 895-919.
• Y. W. Lei and D. X. Zhou, Learning theory of randomized Sparse Kaczmarz method, SIAM Journal on Imaging Sciences 11 (2018), 547-574.
• J. H. Lin and D. X. Zhou, Online learning algorithms can converge comparably fast as batch learning, IEEE Transactions on Neural Networks and Learning Systems 29 (2018), 2367-2378.
• A. Christmann, D. H. Xiang, and D. X. Zhou, Total stability of kernel methods, Neurocomputing 289 (2018), 101-118.
• S. B. Lin and D. X. Zhou, Optimal learning rates for kernel partial least squares, Journal of Fourier Analysis and Applications 24 (2018), 908-933.
• S. B. Lin and D. X. Zhou, Distributed kernel gradient descent algorithms, Constructive Approximation 47 (2018), 249-276.
• C. K. Chui, S. B. Lin, and D. X. Zhou, Construction of neural networks for realization of localized deep learning, Frontiers in Applied Mathematics and Statistics 4:14 (2018). doi: 10.3389/fams.2018.00014

2017:

• S. B. Lin, X. Guo, and D. X. Zhou, Distributed learning with regularized least squares, Journal of Machine Learning Research 18 (92): 1-31, 2017.
• Z. C. Guo, S. B. Lin, and D. X. Zhou, Learning theory of distributed spectral algorithms, Inverse Problems 33 (2017) 074009 (29pp).
• X. Y. Chang, S. B. Lin, and D. X. Zhou, Distributed semi-supervised learning with kernel ridge regression, Journal of Machine Learning Research 18 (46):1-22, 2017.
• Y. Ying and D. X. Zhou, Unregularized online learning algorithms with general loss functions, Applied and Computational Harmonic Analysis 42 (2017), 224-244.
• Z. C. Guo, Y. Ying, and D. X. Zhou, Online regularized learning with pairwise loss functions, Advances in Computational Mathematics 43 (2017), 127-150.
• J. H. Lin, Y. W. Lei, B. Zhang, and D. X. Zhou, Online pairwise learning algorithms with convex loss functions, Information Sciences 406-407 (2017), 57-70.
• Z. C. Guo, D. H. Xiang, X. Guo, and D. X. Zhou, Thresholded spectral algorithms for sparse approximations, Analysis and Applications 15 (2017), 433-455.
• Y. W. Lei and D. X. Zhou, Analysis of online composite mirror descent algorithm, Neural Computation 29 (2017), 825860.
• B. Z. Li, B. L. He, and D. X. Zhou, Approximation on variable exponent spaces by linear integral operators, Journal of Approximation Theory 223 (2017), 29-51.

2016:

• M. Yuan and D. X. Zhou, Minimax Optimal rates of estimation in high dimensional additive models, Annals of Statistics 44 (2016), 2564-2593.
• J. H. Lin, L. Rosasco, and D. X. Zhou, Iterative regularization for learning with convex loss functions, Journal of Machine Learning Research 17 (77):1-38, 2016.
• X. Guo, J. Fan, and Ding-Xuan Zhou, Sparsity and error analysis of empirical feature-based regularization schemes, Journal of Machine Learning Research 17 (89):1-34, 2016.
• J. Fan, T. Hu, Q. Wu and Ding-Xuan Zhou, Consistency analysis of an empirical minimum error entropy algorithm, Applied and Computational Harmonic Analysis 41 (2016), 164-189.
• Y. Ying and D. X. Zhou, Online pairwise learning algorithms, Neural Computation 28 (2016), 743-777.
• T. Hu, Q. Wu and D. X. Zhou, Convergence of gradient descent for minimum error entropy principle in linear regression, IEEE Transactions on Signal Processing 64 (2016), 6571-6579.
• A. Christmann and D. X. Zhou, Learning rates for the risk of kernel-based quantile regression estimators in additive models, Analysis and Applications 14 (2016), 449-477.
• C. A. Micchelli, M. Pontil, Q. Wu, and D. X. Zhou, Error bounds for learning the kernel, Analysis and Applications 14 (2016), 849-868.
• A. Christmann and D. X. Zhou, On the robustness of regularized pairwise learning methods based on kernels, Journal of Complexity 37 (2016), 1-33.

2015:

• J. H. Lin and D. X. Zhou, Learning theory of randomized Kaczmarz algorithm, Journal of Machine Learning Research 16 (2015), 3341-3365.
• L. Q. Li and D. X. Zhou, Learning theory approach to a system identification problem involving atomic norm, Journal of Fourier Analysis and Applications 21 (2015), 734-753.
• T. Hu, J. Fan, Q. Wu and D. X. Zhou, Regularization schemes for minimum error entropy principle, Analysis and Applications 13 (2015), 437-455.

2014:

• D. X. Zhou, Approximation by positive linear operators on variable L^p(·) spaces, Journal of Applied Functional Analysis 9 (2014), 379-391.
• B. Z. Li and D. X. Zhou, Analysis of approximation by linear operators on variable spaces, Abstract and Applied Analysis, Volume 2014 (2014), Article ID 454375, 10 pages.
• A. Y. Chen, J. P. Li, Y. Q. Chen, and D. X. Zhou, Asymptotic behaviour of extinction probability of interacting branching of collision processes, Journal of Applied Probability 51 (2014), 219-234.

2013:

• T. Hu, J. Fan, Q. Wu and D. X. Zhou, Learning theory approach to minimum error entropy criterion, Journal of Machine Learning Research 14 (2013), 377-397.
• Z. C. Guo and D. X. Zhou, Concentration estimates for learning with unbounded sampling, Advances in Computational Mathematics 38 (2013), 207-223.
• D. X. Zhou, Density problem and approximation error in learning theory, Abstract and Applied Analysis, Volume 2013, Article ID 715683, 13 pages.
• D. X. Zhou, On grouping effect of elastic net, Statistics and probability Letters 83 (2013), 2108-2112.
• H. Y. Wang, Q. W. Xiao, and D. X. Zhou, An approximation theory approach to learning with ℓ¹ regularization, Journal of Approximation Theory 167 (2013) 240-258.

2012:

• X. Guo and D. X. Zhou, An empirical feature-based learning algorithm producing sparse approximations, Applied and Computational Harmonic Analysis 32 (2012), 389-400.
• T. Hu, D. H. Xiang, and D. X. Zhou, Online learning for quantile regression and support vector regression, Journal of Statistical Planning and Inference 142 (2012), 3107-3122.
• H. Gonska, J. Prestin, G. Tachev, and D. X. Zhou, Simultaneous approximation by Bernstein operators in Hölder norms, Mathematische Nachrichten 286 (2012), 349-359.
• W. Gao and D. X. Zhou, Convergence of spectral clustering with a general similarity function, Science China Mathematics 42 (2012), 985-994. (in Chinese)
• D. H. Xiang, T. Hu, and D. X. Zhou, Approximation analysis of learning algorithms for support vector regression and quantile regression, J. Appl. Math. 2012 (2012), Article ID 902139, 17 pages.
• A. Y. Chen, J. P. Li, Y. Q. Chen, and D. X. Zhou, Extinction probability of interacting branching collision processes, Advances in Applied Probability 44 (2012), 226-259.

2011:

• L. Shi, Y. L. Feng, and D. X. Zhou, Concentration estimates for learning with ℓ¹-regularizer and data dependent hypothesis spaces, Applied and Computational Harmonic Analysis 31 (2011), 286-302.
• C. Wang and D. X. Zhou, Optimal learning rates for least square regularized regression with unbounded sampling, Journal of Complexity 27 (2011), 55-67.
• D. H. Xiang, T. Hu, and D. X. Zhou, Learning with varying insensitive loss, Appl. Math. Letters 24 (2011), 2107-2109.
• L. Shi and D. X. Zhou, Normal estimation on manifolds by gradient learning, Numerical Linear Algebra with Applications 18 (2011), 249C-259.
• X. J. Zhou, L. Shi, and D. X. Zhou, Non-uniform randomized sampling for multivariate approximation by high order Parzen windows, Can. Math. Bull. 54 (2011), 566-576.

2010:

• S. Mukherjee, Q. Wu and D. X. Zhou, Gradient learning and feature selection on manifolds, Bernoulli 16 (2010), 181-207.
• Q. W. Xiao and D. X. Zhou, Learning by nonsymmetric kernels with data dependent spaces and ℓ¹-regularizer, Taiwan. J. Math. 14 (2010), 1821-1836.
• H. Y. Wang, D. H. Xiang and D. X. Zhou, Moving least-square method in learning theory, J. Approx. Theory 162 (2010), 599-614.
• L. Shi, X. Guo, and D. X. Zhou, Hermite learning with gradient data, J. Comput. Appl. Math. 233 (2010), 3046-3059.
• T. Hu and D. X. Zhou, Online classification with varying Gaussians, Studies in Appl. Math. 124 (2010), 65-83.

2009:

• D. H. Xiang and D. X. Zhou, Classification with Gaussians and convex loss, J. Machine Learning Research 10 (2009), 1447-1468.
• S. Smale and D. X. Zhou, Online learning with Markov sampling, Anal. Appl. 7 (2009), 87-113.
• S. Smale and D. X. Zhou, Geometry on probability spaces, Constr. Approx. 30 (2009), 311-323.
• T. Hu and D. X. Zhou, Online classification with samples drawn from non-identical distributions, J. Machine Learning Research 10 (2009), 2873-2898.
• J. Cai, H. Y. Wang, and D. X. Zhou, Gradient learning in a classification setting by gradient descent, J. Approx. Theory 161 (2009), 674-692.
• X. J. Zhou and D. X. Zhou, High order Parzen windows and noised sampling, Adv. Comput. Math. 31 (2009), 349-368.
• G. B. Ye and D. X. Zhou, SVM learning and L^p approximation by Gaussians on Riemannian manifolds, Anal. Appl. 7 (2009), 309-339.

2008:

• Z. W. Pan, D. H. Xiang, Q. W. Xiao, and D. X. Zhou, Parzen windows for multi-class classification, J. Complexity 24 (2008), 606-618.
• D. X. Zhou, Derivative reproducing properties for kernel methods in learning theory, J. Comput. Appl. Math. 220 (2008), 456-463.
• G. B. Ye and D. X. Zhou, Learning and approximation by Gaussians on Riemannian manifolds, Adv. Comput. Math. 29 (2008), 291-310.
• H. W. Sun and D. X. Zhou, Reproducing kernel Hilbert spaces associated with analytic translation-invariant Mercer kernels, J. Fourier Anal. Appl. 14 (2008), 89-101.
• Q. Wu and D. X. Zhou, Learning with sample dependent hypothesis spaces, Computers and Mathematics with Applications 56 (2008), 2896-2907.
• X. M. Dong and D. X. Zhou, Learning gradients by a gradient descent algorithm, J. Math. Anal. Appl. 341 (2008), 1018-1027.

2007:

• S. Smale and D. X. Zhou, Learning theory estimates via integral operators and their approximations, Constr. Approx. 26 (2007), 153-172.
• G. B. Ye and D. X. Zhou, Fully online classification by regularization, Appl. Comput. Harmonic Anal. 23 (2007), 198-214.
• Q. Wu, Y. Ying, and D. X. Zhou, Multi-kernel regularized classifiers, J. Complexity 23 (2007), 108-134.
• Y. Ying and D. X. Zhou, Learnability of Gaussians with flexible variances, J. Machine Learning Research 8 (2007), 249-276.

2006:

• Y. Ying and D. X. Zhou, Online regularized classification algorithms, IEEE Trans. Inform. Theory 52 (2006), 4775-4788.
• S. Mukherjee and D. X. Zhou, Learning coordinate covariances via gradients, J. Machine Learning Research 7 (2006), 519-549.
• Q. Wu, Y. Ying, and D. X. Zhou, Learning rates of least-square regularized regression, Found. Comput. Math. 6 (2006), 171-192.
• D. X. Zhou and K. Jetter, Approximation with polynomial kernels and SVM classifiers, Adv. Comput. Math. 25 (2006), 323-344.
• Q. Wu and D. X. Zhou, Analysis of support vector machine classification, J. Comput. Anal. Appl. 8 (2006), 99-119.

2005:

• S. Smale and D. X. Zhou, Shannon sampling II. Connections to learning theory, Appl. Comput. Harmonic Anal. 19 (2005), 285-302.
• Q. Wu and D. X. Zhou, SVM soft margin classifiers: linear programming versus quadratic programming, Neural Computation 17 (2005), 1160-1187.

2004:

• S. Smale and D. X. Zhou, Shannon sampling and function reconstruction from point values, Bull. Amer. Math. Soc. 41 (2004), 279-305.
• F. Cucker, S. Smale, and D. X. Zhou, Modelling language evolution, Found. Comput. Math. 4 (2004), 315-343.
• D. R. Chen, Q. Wu, Y. Ying, and D. X. Zhou, Support vector machine soft margin classifiers: error analysis, J. Machine Learning Research 5 (2004), 1143-1175.

2003:

• R. Q. Jia, J. Z. Wang, and D. X. Zhou, Compactly supported wavelet bases for Sobolev spaces, Appl. Comput. Harmonic Anal. 15 (2003), 224-241.
• D. X. Zhou, Capacity of reproducing kernel spaces in learning theory, IEEE Trans. Inform. Theory 49 (2003), 1743-1752.
• G. Plonka and D. X. Zhou, Properties of locally linearly independent refinable function vectors, J. Approx. Theory 122 (2003), 24-41.
• C. A. Micchelli and D. X. Zhou, Refinable functions: positivity and interpolation, Anal. Appl. 1 (2003), 243-264.
• S. Smale and D. X. Zhou, Estimating the approximation error in learning theory, Anal. Appl. 1 (2003), 17-41.

2002:

• D. X. Zhou, The covering number in learning theory, J. Complexity 18 (2002), 739-767.
• D. X. Zhou, Interpolatory orthogonal multiwavelets and refinable functions, IEEE Trans. Signal Processing 50 (2002), 520-527.
• D. X. Zhou, Two-scale homogeneous functions in wavelet analysis, J. Fourier Anal. Appl. 8 (2002), 565-580.
• M. Nielsen and D. X. Zhou, Mean size of wavelet packets, Appl. Comput. Harmonic Anal. 13 (2002), 22-34.
• H. L. Cheung, C Tang, and D. X. Zhou, Supports of locally linearly independent M-refinable functions, attractors of iterated function systems and tilings, Adv. Comput. Math. 17 (2002), 257-268.

2001:

• G. Strang and D. X. Zhou, The limits of refinable functions, Trans. Amer. Math. Soc. 353 (2001), 1971-1984.
• D. X. Zhou, Norms concerning subdivision sequences and their applications in wavelets, Appl. Comput. Harmonic Anal. 11 (2001), 329-346.
• D. X. Zhou, Spectra of subdivision operators, Proc. Amer. Math. Soc. 129 (2001), 191-202.
• R. Q. Jia, K. S. Lau and D. X. Zhou, L_p-solutions of vector refinement equations, J. Fourier Anal. Appl. 7 (2001), 144-169.
• D. X. Zhou, Self-similar lattice tilings and subdivision schemes, SIAM J. Math. Anal. 33 (2001), 1-15.
• G. Boyd, C. A. Micchelli, G. Strang, and D. X. Zhou, Binomial matrices, Adv. Comput. Math. 14 (2001), 379-391.

2000:

• T. N. T. Goodman, R. Q. Jia and D. X. Zhou, Local linear independence of refinable vectors of functions, Proc. Royal Soc. Edinburgh 130A (2000), 813-826.
• D. X. Zhou, Multiple refinable Hermite interpolants, J. Approx. Theory 102 (2000), 46-71.

1999:

• R. Q. Jia, S. D. Riemenschneider and D. X. Zhou, Smoothness of multiple refinable functions and multiple wavelets, SIAM J. Matrix Anal. Appl. 21 (1999), 1-28.
• R. Q. Jia and D. X. Zhou, Convergence of subdivision schemes associated with nonnegative masks, SIAM J. Matrix Anal. Appl. 21 (1999), 418-430.
• D. X. Zhou, Solvability of linear systems of PDE's with constant coefficients, Proc. Amer. Math. Soc. 127 (1999), 2013-2017.
• C. Cottin, I. Gavrea, H. H. Gonska, D. Kacso and D. X. Zhou, Global smoothness preservation and the variation-diminishing property, J. Inequal. Appl. 4 (1999), 91-114.

1998:

• D. X. Zhou, The p-norm joint spectral radius for even integers, Methods and Applications of Analysis 5 (1998), 39-54.
• R. Q. Jia, S. D. Riemenschneider and D. X. Zhou, Vector subdivision schemes and multiple wavelets, Math. Comp. 67 (1998), 1533-1563.
• G. Strang and D. X. Zhou, Inhomogeneous refinement equations, J. Fourier Anal. Appl. 4 (1998), 733-747.
• D. X. Zhou, Some characterizations for Box spline wavelets and linear diophantine equations, Rocky Mountain J. Math. 28 (1998), 1539-1560.

1997:

• R. Q. Jia, S. D. Riemenschneider and D. X. Zhou, Approximation by multiple refinable functions, Canadian J. Math. 49 (1997), 944-962.
• D. X. Zhou, Existence of multiple refinable distributions, Michigan Math J. 44 (1997), 317-329.
• D. X. Zhou, Extendibility of rational matrices, J. Approx. Theory 88 (1997), 272-274.
• D. X. Zhou, B. Z. Li, and S. Li, A Korovkin theorem for σ(L_∞,L₁) approximation, (in Chinese) Acta Math. Appl. Sinica 20 (1997), 409-412.
• H. H. Gonska and D. X. Zhou, Design of Wilson-Fowler splines, Studia Univ. Babes-Bolyai Math. 42 (1997), 89-100.

1996:

• D. X. Zhou, Refinable functions, multiresolution analysis and Haar bases, SIAM J. Math. Anal. 27 (1996), 891-904.
• D. Mache and D. X. Zhou, Characterization theorems for the approximation by a family of operators, J. Approx. Theory 84 (1996), 145-161.
• D. X. Zhou, Box splines with rational directions and linear diophantine equations, J. Math. Anal. Appl. 203 (1996), 270-277.
• D. X. Zhou, Linear dependence relations in wavelets and tilings, Linear Algebra Appl. 249 (1996), 311-323.

1995:

• K. Jetter and D. X. Zhou, Order of linear approximation in shift-invariant spaces, Constr. Approx. 11 (1995), 423-438.
• D. X. Zhou and K. Jetter, Characterization of correctness of cardinal interpolation with shifted three-directional Box splines, Proc. Roy. Soc. Edinburgh 125 (1995), 931-937.
• D. X. Zhou, Construction of real-valued wavelets by symmetry, J. Approx. Theory 81 (1995), 323-331.
• D. X. Zhou, On smoothness characterized by Bernstein type operators, J. Approx. Theory 81 (1995), 303-315.
• Z. R. Guo and D. X. Zhou, Local approximation by modified Szász operators, J. Math. Anal. Appl. 195 (1995), 323-334.
• D. X. Zhou, On a problem of Gonska, Results in Math. 28 (1995), 169-183.
• H. H. Gonska and D. X. Zhou, Local smoothness of functions and Bernstein-Durrmeyer operators, Computers and Mathematics with Applications, 30 (1995), 83-101.
• H. H. Gonska and D. X. Zhou, Using wavelets for Szász-type operators, Rev. Anal. Numr. Thor. Approx. 24 (1995), 131-145.
• H. H. Gonska and D. X. Zhou, On an extremal problem for Bernstein operators, Serdica Mathematical Journal 21 (1995), 137-150.
• P. C. Xuan and D. X. Zhou, The order of convergence for weighted Baskakov operators, (in Chinese) Acta Math. Appl. Sinica 18 (1995), 129-139.
• D. Mache and D. X. Zhou, Best direct and converse results for Langrang-type operators, Approx. Theory Appl. 11 (1995), 76-93.
• K. Jetter and D. X. Zhou, Seminorm and full norm order of linear approximation from shift-invariant spaces, Rendiconti del Seminario Matematico e Fisico di Milano vol. LXV (1995), 277-302.

1994:

• D. X. Zhou, Weighted approximation by multidimensional Bernstein operators, J. Approx. Theory 76 (1994), 403-422.
• M. D. Ye and D. X. Zhou, A class of operators by means of three-diagonal matrices, J. Approx. Theory 78 (1994), 239-259.
• D. X. Zhou, Weighted approximation by Szász-Mirakjan operators, J. Approx. Theory 76 (1994), 393-402.
• D. X. Zhou, A note on derivatives of Bernstein polynomials, J. Approx. Theory 78 (1994), 147-150.
• D. X. Zhou, On wavelets in L₁, Acta Math. Appl. Sinica 10 (1994), 69-74.
• N. S. Zhang and D. X. Zhou, Direct and inverse theorems for Jackson operators in Besov spaces, Acta Math. Appl. Sinica 17 (1994), 355-363.
• D. X. Zhou, Z. Q. Zhang and P. C. Xuan, On L_p approximation by multidimensional Baskakov-Durrmeyer operators, Math. Appl. 7 (1994), 119-123.

1993:

• D. X. Zhou, On a paper of Mazhar and Totik, J. Approx. Theory 72 (1993), 290-300.
• D. X. Zhou, Converse theorems for multidimensional Kantorovich operators, Anal. Math. 19 (1993), 85-100.
• D. X. Zhou and N. S. Zhang, Besov spaces of Ditzian-Totik type and Bernstein-Durrmeyer operators, J. Math. Res. Exp. 13 (1993), 499-504.

1992:

• D. X. Zhou, On a conjecture of Z. Ditzian, J. Approx. Theory 69 (1992), 167-172.
• D. X. Zhou, Inverse theorems for multidimensional Bernstein-Durrmeyer operators in L_p, J. Approx. Theory 70 (1992), 68-93.
• Z. R. Guo and D. X. Zhou, Approximation theorems for modified Szász operators, Acta Sci. Math. (Szeged) 56 (1992), 311-321.
• D. X. Zhou, Wavelet transform, Toeplitz type operators and decomposition of functions on the upper-half plane, Rev. Anal. Numér. Théor. Approx. 21 (1992), 89-100.
• D. X. Zhou, Rate of convergence for Bernstein operators with Jacobi-weights, (in Chinese) Acta Math. Sinica 35 (1992), 331-338.
• D. X. Zhou, Multivariate orthogonal bases of wavelets, J. Math. Res. Exp. 12 (1992), 179-182.
• D. X. Zhou and B. Ning, Wavelets and Besov spaces , Chinese J. Math. 12 (1992), 276-280.
• D. X. Zhou, A note on Bernstein type operators, Approx. Theory Appl. 8 (1992), 97-100.
• D. X. Zhou, P. C. Xuan and Z. Q. Zhang, Uniform approximation by multidimensional Szász-Mirakjan operators, J. Math. Res. Exp. 12 (1992), 187-192.

1991:

• D. X. Zhou, L_p–Inverse theorems for Beta operators, J. Approx. Theory 66 (1991), 279-287.
• D. X. Zhou, Inverse theorems in L_p for some multidimensional positive linear operators, Chinese Ann. Math. 12 (1991), 141-146.

1990:

• D. X. Zhou, Uniform approximation by some Durrmeyer operators, Approx. Theory Appl. 6 (1990), 87-100.
• D. X. Zhou, Inverse theorems for some multidimensional operators, Approx. Theory Appl. 6 (1990), 25-39.
• D. X. Zhou, A note on pseudo ideals of fields, J. Math. Res. Exp. 10 (1990), 421-422.

Books

• D. X. Zhou (editor), Wavelet Analysis: Twenty Years' Developments, World Scientific Press, 2002.
• F. Cucker and D. X. Zhou, Learning Theory: An Approximation Theory Viewpoint, Cambridge University Press, 2007.
• D. Dai, H. H. Dai, T. Tang, and D. X. Zhou (editors), The Collected Works of Roderick S. C. Wong, Volumes 1, 2, 3, World Scientific Press, 2016.
• I. Pesenson, Q. Thong Le Gia, H. Mhaskar, and D. X. Zhou (editors), Frames and Other Bases in Abstract and Function Spaces, Novel Methods in Harmonic Analysis, Volume 1, Birkhäuser, 2017.
• I. Pesenson, Q. Thong Le Gia, H. Mhaskar, and D. X. Zhou (editors), Recent Applications of Harmonic Analysis to Function Spaces, Differential Equations, and Data Science, Novel Methods in Harmonic Analysis, Volume 2, Birkhäuser, 2017.
• (Book Chapter) D. X. Zhou, Machine Learning Algorithms, in Encyclopedia of Applied and Computational Mathematics, edited by B. Engquist, Springer-Verlag, Berlin, Heidelberg, 2015, pp. 839-841.

Students and Alumni

Ph.D. Students

• Zhiying Fang, August 2016 -
• Tong Mao, September 2018 -
• Zhongjie Shi, September 2018 -
• Linhao Song, September 2019 - (co-supervising)
• Shuo Huang, September 2019 - (co-supervising)

Alumni

• M.Phil. graduate Hoi Ling Cheung, 2002, Hong Kong Institute of Vocational Education
• Ph.D. graduate Qiang Wu, 2005, Middle Tennessee State University
• Ph.D. graduate Gui-Bo Ye, Fudan University, 2007, HKUST
• Ph.D. graduate Daohong Xiang, 2009, Zhejiang Normal University
• Ph.D. graduate Ting Hu, 2009, Wuhan University
• Ph.D. graduate Hongyan Wang, 2009
• Ph.D. graduate Jia Cai, 2009, Guangdong University of Business Studies
• Ph.D. graduate Xiangjun Zhou, 2009, Beijing Shuye Tech. Ltd
• Ph.D. graduate Quanwu Xiao, 2009, ctrip.com
• Ph.D. graduate Zhiwei Pan, 2009, Bank of Communications, Shanghai
• Ph.D. graduate Lei Shi, 2010, Fudan University
• Ph.D. graduate Cheng Wang, 2010, Huizhou University
• Ph.D. graduate Shaogao Lv, 2011, Nanjing Audit University
• Ph.D. graduate Zhengchu Guo, 2011, Zhejiang University
• Ph.D. graduate Xin Guo, 2011, The Hong Kong Polytechnic University
• Ph.D. graduate Yunlong Feng, 2012, State University of New York at Albany
• Ph.D. graduate Yulong Zhao, 2013, FDT AI, Hong Kong
• Ph.D. graduate Jun Fan, 2013, Hong Kong Baptist University
• Ph.D. graduate Martin Boissier, 2016, Ambi Labs, Hong Kong
• Ph.D. graduate Bo Zhang, 2017, Hong Kong Baptist University
• Ph.D. graduate Xiaming Chen, 2018, Shantou University
• Postdoc Yiming Ying, 2003-2005, State University of New York at Albany
• Postdoc Junhong Lin, 2013-2015, Zhejiang University
• Postdoc Shaobo Lin, 2015-2018, Xi'an Jiaotong University
• Postdoc Yunwen Lei, 2015-2017, Technical University of Kaiserslautern

Editorial Board Membership

• Editor-in-Chief, Analysis and Applications, World Scientific Press, 2013-present
• Editor-in-Chief, Mathematical Foundations of Computing, AIMS, 2019-present
• Advances in Computational Mathematics, Springer, 2006-2013
• Applied and Computational Harmonic Analysis, Elsevier, 2010-present
• Complex Analysis and Operator Theory, Birkhauser, 2007-present
• Journal of Approximation Theory, Elsevier, 2010-present
• Journal of Complexity, Elsevier, 2018-present
• Journal of Computational Analysis and Applications, Eudoxus, 1999-present
• Applied Mathematics-A Journal of Chinese Universities, Springer, 2012-present
• Journal of Mathematics, Hindawi, 2012-present
• Journal of Intelligent Learning Systems and Applications, Scientific Research, 2011-present
• Pure Mathematics, Hans, 2011-present (in Chinese)
• Revue d'Analyse Numéique et de Théorie de l'Approximation (Journal of Numerical Analysis and Approximation Theory), Romanian Academy of Sciences, 2015-present
• Mathematics of Computation and Data Science, Frontiers, 2016-present
• Geometry, Imaging and Computing, International Press, 2018-present
• Communications on Pure and Applied Analysis, AIMS, 2018-present
• Econometrics and Statistics, Part B: Statistics, Elsevier, 2019-present
• Editor, book series "Progress in Data Science", World Scientific Press, 2018-present