Ding-Xuan Zhou

发表

Smoothness of Multiple Refinable Functions and Multiple Wavelets

Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou, 1999, SIAM J. Matrix Anal. Appl..

Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition pdf

Ming Yuan, Ding-Xuan Zhou, M. Yuan, 2015, ArXiv.

Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping

Shao-Bo Lin, Yunwen Lei, Ding-Xuan Zhou, 2019, J. Mach. Learn. Res..

Total stability of kernel methods

Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou, 2017, Neurocomputing.

Fast Convergence of Online Pairwise Learning Algorithms

Siwei Lyu, Yiming Ying, Ding-Xuan Zhou, 2016, AISTATS.

Online Pairwise Learning Algorithms

Yiming Ying, Ding-Xuan Zhou, Ding-Xuan Zhou, 2016, Neural Computation.

Supports of Locally Linearly Independent M-Refinable Functions, Attractors of Iterated Function Systems and Tilings

Ding-Xuan Zhou, Hoi Ling Cheung, Canqin Tang, 2002, Adv. Comput. Math..

Normal estimation on manifolds by gradient learning

Lei Shi, Ding-Xuan Zhou, Ding-Xuan Zhou, 2011, Numer. Linear Algebra Appl..

On the robustness of regularized pairwise learning methods based on kernels

Andreas Christmann, Ding-Xuan Zhou, Ding-Xuan Zhou, 2015, J. Complex..

Optimal learning rates for least squares regularized regression with unbounded sampling

Cheng Wang, Ding-Xuan Zhou, Ding-Xuan Zhou, 2011, J. Complex..

Learning Theory: An Approximation Theory Viewpoint: References

Felipe Cucker, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007 .

Parzen windows for multi-class classification

Dao-Hong Xiang, Ding-Xuan Zhou, Quan-Wu Xiao, 2008, J. Complex..

Online Learning Algorithms Can Converge Comparably Fast as Batch Learning

Ding-Xuan Zhou, Junhong Lin, Ding-Xuan Zhou, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Online Learning with Samples Drawn from Non-identical Distributions

Ting Hu, Ding-Xuan Zhou, 2009, J. Mach. Learn. Res..

High order Parzen windows and randomized sampling

Ding-Xuan Zhou, Xiang-Jun Zhou, Ding-Xuan Zhou, 2009, Adv. Comput. Math..

Learning Theory: An Approximation Theory Viewpoint: On the bias–variance problem

Felipe Cucker, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007 .

Convergence of Online Mirror Descent Algorithms pdf

Yunwen Lei, Ding-Xuan Zhou, 2018, ArXiv.

Iterative Regularization for Learning with Convex Loss Functions

Lorenzo Rosasco, Ding-Xuan Zhou, Junhong Lin, 2015, J. Mach. Learn. Res..

Wavelet analysis : twenty year's developments : proceedings of the International Conference of Computational Harmonic Analysis, Hong Kong, China, 4-8 June 2001

Ding-Xuan Zhou, 2002 .

Online regularized learning with pairwise loss functions

Yiming Ying, Ding-Xuan Zhou, Zheng-Chu Guo, 2016, Advances in Computational Mathematics.

Convergence of online mirror descent

Yunwen Lei, Ding-Xuan Zhou, 2018 .

Universality of Deep Convolutional Neural Networks pdf

Ding-Xuan Zhou, Ding-Xuan Zhou, 2018, Applied and Computational Harmonic Analysis.

Online pairwise learning algorithms with convex loss functions

Bo Zhang, Yunwen Lei, Ding-Xuan Zhou, 2017, Inf. Sci..

Theory of Deep Convolutional Neural Networks II: Spherical Analysis

Shuo Huang, Ding-Xuan Zhou, Han Feng, 2020, Neural Networks.

Multi-kernel regularized classifiers

Yiming Ying, Ding-Xuan Zhou, Qiang Wu, 2007, J. Complex..

Data-Dependent Generalization Bounds for Multi-Class Classification

Marius Kloft, Yunwen Lei, Ding-Xuan Zhou, 2017, IEEE Transactions on Information Theory.

Properties of locally linearly independent refinable function vectors

Gerlind Plonka-Hoch, Ding-Xuan Zhou, Ding-Xuan Zhou, 2003, J. Approx. Theory.

Consistency Analysis of an Empirical Minimum Error Entropy Algorithm pdf

Jun Fan, Ting Hu, Ding-Xuan Zhou, 2014, ArXiv.

Gradient learning in a classification setting by gradient descent

Ding-Xuan Zhou, Jia Cai, Hongyan Wang, 2009, J. Approx. Theory.

Learning theory approach to minimum error entropy criterion

Jun Fan, Ting Hu, Ding-Xuan Zhou, 2012, J. Mach. Learn. Res..

Ding-Xuan Zhou, Ding-Xuan Zhou, 2001, SIAM J. Math. Anal..

Modified Fejér sequences and applications

Lorenzo Rosasco, Silvia Villa, Ding-Xuan Zhou, 2015, Comput. Optim. Appl..

Support Vector Machine Soft Margin Classifiers: Error Analysis

Yiming Ying, Ding-Xuan Zhou, Qiang Wu, 2004, J. Mach. Learn. Res..

Modeling Language Evolution

Felipe Cucker, Stephen Smale, Ding-Xuan Zhou, 2004, Found. Comput. Math..

Learnability of Gaussians with Flexible Variances

Yiming Ying, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007, J. Mach. Learn. Res..

Learning with varying insensitive loss

Dao-Hong Xiang, Ding-Xuan Zhou, Ting Hu, 2011, Appl. Math. Lett..

Learning Theory: An Approximation Theory Viewpoint (Cambridge Monographs on Applied & Computational Mathematics)

Felipe Cucker, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007 .

Deep Net Tree Structure for Balance of Capacity and Approximation Ability

Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou, 2019, Front. Appl. Math. Stat..

Towards Understanding the Spectral Bias of Deep Learning pdf

Yuan Cao, Quanquan Gu, Ding-Xuan Zhou, 2019, IJCAI.

Approximation with polynomial kernels and SVM classifiers

Kurt Jetter, Ding-Xuan Zhou, Ding-Xuan Zhou, 2006, Adv. Comput. Math..

Learning theory of randomized Kaczmarz algorithm

Ding-Xuan Zhou, Junhong Lin, Ding-Xuan Zhou, 2015, J. Mach. Learn. Res..

The covering number in learning theory

Ding-Xuan Zhou, Ding-Xuan Zhou, 2002, J. Complex..

Convergence of Subdivision Schemes Associated with Nonnegative Masks

Rong-Qing Jia, Ding-Xuan Zhou, 2000, SIAM J. Matrix Anal. Appl..

Learning Theory: An Approximation Theory Viewpoint: Index

Felipe Cucker, Ding-Xuan Zhou, Ding-Xuan Zhou, 2007 .

Online Pairwise Learning Algorithms with Kernels pdf

Yiming Ying, Ding-Xuan Zhou, 2015, ArXiv.

Hermite learning with gradient data

Lei Shi, Ding-Xuan Zhou, Xin Guo, 2010, J. Comput. Appl. Math..

Analysis of Online Composite Mirror Descent Algorithm

Yunwen Lei, Ding-Xuan Zhou, Ding-Xuan Zhou, 2017, Neural Computation.

Modelling Language Evolution ⁄y

Felipe Cucker, Steve Smale, Ding-Xuan Zhou, 2003 .

Vector subdivision schemes and multiple wavelets

Rong-Qing Jia, Sherman D. Riemenschneider, Ding-Xuan Zhou, 1998, Math. Comput..

Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes

Jun Fan, Ding-Xuan Zhou, Xin Guo, 2016, J. Mach. Learn. Res..

Distributed Semi-supervised Learning with Kernel Ridge Regression

Xiangyu Chang, Ding-Xuan Zhou, Shaobo Lin, 2017, J. Mach. Learn. Res..

Classification with Gaussians and Convex Loss

Dao-Hong Xiang, Ding-Xuan Zhou, Ding-Xuan Zhou, 2009, J. Mach. Learn. Res..

Concentration estimates for learning with unbounded sampling

Ding-Xuan Zhou, Zheng-Chu Guo, Ding-Xuan Zhou, 2013, Adv. Comput. Math..

Learning Rates of Least-Square Regularized Regression

Yiming Ying, Ding-Xuan Zhou, Qiang Wu, 2006, Found. Comput. Math..

Distributed Learning with Regularized Least Squares pdf

Ding-Xuan Zhou, Shaobo Lin, Xin Guo, 2016, J. Mach. Learn. Res..

Unregularized Online Learning Algorithms with General Loss Functions pdf

Yiming Ying, Ding-Xuan Zhou, Ding-Xuan Zhou, 2015, ArXiv.

Learning with sample dependent hypothesis spaces

Ding-Xuan Zhou, Qiang Wu, Ding-Xuan Zhou, 2008, Comput. Math. Appl..

Binomial Matrices

Charles A. Micchelli, Gilbert Strang, Ding-Xuan Zhou, 2001, Adv. Comput. Math..

Moving least-square method in learning theory

Dao-Hong Xiang, Ding-Xuan Zhou, Hong-Yan Wang, 2010, J. Approx. Theory.

Learning and approximation by Gaussians on Riemannian manifolds

Ding-Xuan Zhou, Gui-Bo Ye, Ding-Xuan Zhou, 2009, Adv. Comput. Math..

Learning Coordinate Covariances via Gradients

Sayan Mukherjee, Ding-Xuan Zhou, Ding-Xuan Zhou, 2006, J. Mach. Learn. Res..

Construction of Neural Networks for Realization of Localized Deep Learning pdf

Charles K. Chui, Ding-Xuan Zhou, Shaobo Lin, 2018, Front. Appl. Math. Stat..

Capacity of reproducing kernel spaces in learning theory

Ding-Xuan Zhou, Ding-Xuan Zhou, 2003, IEEE Transactions on Information Theory.

Interpolatory orthogonal multiwavelets and refinable functions

Ding-Xuan Zhou, 2002, IEEE Trans. Signal Process..

Learning Theory of Randomized Sparse Kaczmarz Method

Yunwen Lei, Ding-Xuan Zhou, Ding-Xuan Zhou, 2018, SIAM J. Imaging Sci..

Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression

Ting Hu, Dao-Hong Xiang, Ding-Xuan Zhou, 2012, J. Appl. Math..

Online Regularized Classification Algorithms

Yiming Ying, Ding-Xuan Zhou, Ding-Xuan Zhou, 2006, IEEE Transactions on Information Theory.

Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression

Ting Hu, Ding-Xuan Zhou, Qiang Wu, 2016, IEEE Transactions on Signal Processing.

Generalization Error Bounds for Extreme Multi-class Classification pdf

Marius Kloft, Yunwen Lei, Ürün Dogan, 2017, ArXiv.

Deep Neural Networks for Rotation-Invariance Approximation and Learning pdf

Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou, 2019, Analysis and Applications.

Approximation on variable exponent spaces by linear integral operators

Ding-Xuan Zhou, Bing-Zheng Li, Bo-Lu He, 2017, J. Approx. Theory.

Radial Basis Function Approximation with Distributively Stored Data on Spheres pdf

Shao-Bo Lin, Ding-Xuan Zhou, Han Feng, 2021, ArXiv.

Universal Consistency of Deep Convolutional Neural Networks pdf

Shao-Bo Lin, Kaidong Wang, Ding-Xuan Zhou, 2021, IEEE Transactions on Information Theory.

Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions

Ding-Xuan Zhou, Tong Mao, Zhongjie Shi, 2021, Neural Networks.

Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets pdf

Shao-Bo Lin, Ding-Xuan Zhou, Yao Wang, 2021, ArXiv.

Distributed regularized least squares with flexible Gaussian kernels

Ting Hu, Ding-Xuan Zhou, 2021, Applied and Computational Harmonic Analysis.

Robust kernel-based distribution regression

Ding-Xuan Zhou, Daniel W C Ho, Zhongjie Shi, 2021, Inverse Problems.

Towards Understanding the Spectral Bias of Deep Learning

Yuan Cao, Quanquan Gu, Ding-Xuan Zhou, 2021, IJCAI.

Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints

Wotao Yin, Jinshan Zeng, Ding-Xuan Zhou, 2021, Journal of Scientific Computing.

Kernel gradient descent algorithm for information theoretic learning

Ting Hu, Ding-Xuan Zhou, Qiang Wu, 2021, J. Approx. Theory.

Weighted approximation by multidimensional Bernstein operators

Ding-Xuan Zhou, 1994 .

Distributed kernel gradient descent algorithm for minimum error entropy principle

Ting Hu, Ding-Xuan Zhou, Qiang Wu, 2020 .

An empirical feature-based learning algorithm producing sparse approximations

Ding-Xuan Zhou, Xin Guo, 2012 .

Refinable Functions: Positivity and Interpolation

Charles A. Micchelli, Ding-Xuan Zhou, 2003 .

Analysis of Approximation by Linear Operators on Variable Spaces and Applications in Learning Theory

Ding-Xuan Zhou, Bing-Zheng Li, Ding-Xuan Zhou, 2014 .

Global smoothness preservation and the variation-diminishing property

Ding-Xuan Zhou, Ioan Gavrea, Claudia Cottin, 1999 .

Distributed Kernel-Based Gradient Descent Algorithms

Ding-Xuan Zhou, Shaobo Lin, Ding-Xuan Zhou, 2018 .

Norms Concerning Subdivision Sequences and Their Applications in Wavelets

Ding-Xuan Zhou, 2001 .

Analysis of Singular Value Thresholding Algorithm for Matrix Completion

Ding-Xuan Zhou, Ding-Xuan Zhou, Yunwen Lei, 2019, Journal of Fourier Analysis and Applications.

Construction of Real-Valued Wavelets by Symmetry

Ding-Xuan Zhou, Ding-Xuan Zhou, 1995 .

Learning Theory: An Approximation Theory Viewpoint: Basic hypothesis spaces

Felipe Cucker, Ding-Xuan Zhou, 2007 .

The limits of refinable functions

Gilbert Strang, Ding-Xuan Zhou, 2001 .

Modelling Language Evolution ∗†

Felipe Cucker, Ding-Xuan Zhou, S. Smale, 2003 .

Stability of refinable functions, multiresolution analysis, and Haar bases

Ding-Xuan Zhou, Ding-Xuan Zhou, 1996 .

A note on derivatives of Bernstein polynomials

Ding-Xuan Zhou, 1994 .

Converse theorems for multidimensional Kantorovich operators

Ding-Xuan Zhou, Ding-Xuan Zhou, 1993 .

Thresholded spectral algorithms for sparse approximations

Dao-Hong Xiang, Ding-Xuan Zhou, Zheng-Chu Guo, 2017 .

Inhomogeneous refinement equations

Gilbert Strang, Ding-Xuan Zhou, 1998 .

On Smoothness Characterized by Bernstein Type Operators

Ding-Xuan Zhou, Ding-Xuan Zhou, 1995 .

On grouping effect of elastic net

Ding-Xuan Zhou, Ding-Xuan Zhou, 2013 .