2024
SignReLU neural network and its approximation ability.
J. Comput. Appl. Math., April, 2024
Sketching with Spherical Designs for Noisy Data Fitting on Spheres.
SIAM J. Sci. Comput., February, 2024
On the rates of convergence for learning with convolutional neural networks.
CoRR, 2024
Nonlinear functional regression by functional deep neural network with kernel embedding.
CoRR, 2024
2023
Rates of approximation by ReLU shallow neural networks.
J. Complex., December, 2023
Generalization Analysis of CNNs for Classification on Spheres.
IEEE Trans. Neural Networks Learn. Syst., September, 2023
Approximation of smooth functionals using deep ReLU networks.
Neural Networks, September, 2023
Deep learning theory of distribution regression with CNNs.
Adv. Comput. Math., August, 2023
Generalization Analysis of Pairwise Learning for Ranking With Deep Neural Networks.
Neural Comput., June, 2023
Federated learning for minimizing nonsmooth convex loss functions.
Math. Found. Comput., 2023
Lifting the Veil: Unlocking the Power of Depth in Q-learning.
CoRR, 2023
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos.
CoRR, 2023
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks.
CoRR, 2023
Classification with Deep Neural Networks and Logistic Loss.
CoRR, 2023
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning.
CoRR, 2023
Learning Theory of Distribution Regression with Neural Networks.
CoRR, 2023
Nonparametric regression using over-parameterized shallow ReLU neural networks.
CoRR, 2023
Optimal Estimates for Pairwise Learning with Deep ReLU Networks.
CoRR, 2023
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks.
CoRR, 2023
Distributed Gradient Descent for Functional Learning.
CoRR, 2023
Approximation of Nonlinear Functionals Using Deep ReLU Networks.
CoRR, 2023
Optimal rates of approximation by shallow ReLU<sup>k</sup> neural networks and applications to nonparametric regression.
CoRR, 2023
Sketching with Spherical Designs for Noisy Data Fitting on Spheres.
CoRR, 2023
Generalization Analysis for Contrastive Representation Learning.
Proceedings of the International Conference on Machine Learning, 2023
2022
Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data.
IEEE Trans. Neural Networks Learn. Syst., 2022
Universal Consistency of Deep Convolutional Neural Networks.
IEEE Trans. Inf. Theory, 2022
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization.
IEEE Trans. Pattern Anal. Mach. Intell., 2022
CNN models for readability of Chinese texts.
Math. Found. Comput., 2022
Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints.
J. Sci. Comput., 2022
A new activation for neural networks and its approximation.
CoRR, 2022
Approximation analysis of CNNs from feature extraction view.
CoRR, 2022
Differentially Private Stochastic Gradient Descent with Low-Noise.
CoRR, 2022
Attention Enables Zero Approximation Error.
CoRR, 2022
Approximation of functions from Korobov spaces by deep convolutional neural networks.
Adv. Comput. Math., 2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Enhancing Automatic Readability Assessment with Pre-training and Soft Labels for Ordinal Regression.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022
2021
Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere.
SIAM J. Numer. Anal., 2021
Theory of deep convolutional neural networks III: Approximating radial functions.
Neural Networks, 2021
On ADMM in Deep Learning: Convergence and Saturation-Avoidance.
J. Mach. Learn. Res., 2021
Kernel gradient descent algorithm for information theoretic learning.
J. Approx. Theory, 2021
Radial Basis Function Approximation with Distributively Stored Data on Spheres.
CoRR, 2021
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets.
CoRR, 2021
Robust Kernel-based Distribution Regression.
CoRR, 2021
Towards Understanding the Spectral Bias of Deep Learning.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021
2020
Theory of deep convolutional neural networks: Downsampling.
Neural Networks, 2020
Theory of deep convolutional neural networks II: Spherical analysis.
Neural Networks, 2020
Distributed Kernel Ridge Regression with Communications.
J. Mach. Learn. Res., 2020
Optimal learning rates for distribution regression.
J. Complex., 2020
2019
Data-Dependent Generalization Bounds for Multi-Class Classification.
IEEE Trans. Inf. Theory, 2019
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping.
J. Mach. Learn. Res., 2019
Deep Net Tree Structure for Balance of Capacity and Approximation Ability.
Frontiers Appl. Math. Stat., 2019
Fast Polynomial Kernel Classification for Massive Data.
CoRR, 2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning.
CoRR, 2019
Optimal Stochastic and Online Learning with Individual Iterates.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
2018
Online Learning Algorithms Can Converge Comparably Fast as Batch Learning.
IEEE Trans. Neural Networks Learn. Syst., 2018
Learning Theory of Randomized Sparse Kaczmarz Method.
SIAM J. Imaging Sci., 2018
Total stability of kernel methods.
Neurocomputing, 2018
Construction of Neural Networks for Realization of Localized Deep Learning.
Frontiers Appl. Math. Stat., 2018
Universality of Deep Convolutional Neural Networks.
CoRR, 2018
Convergence of Online Mirror Descent Algorithms.
CoRR, 2018
Modified Fejér sequences and applications.
Comput. Optim. Appl., 2018
2017
Analysis of Online Composite Mirror Descent Algorithm.
Neural Comput., 2017
Distributed Learning with Regularized Least Squares.
J. Mach. Learn. Res., 2017
Distributed Semi-supervised Learning with Kernel Ridge Regression.
J. Mach. Learn. Res., 2017
Approximation on variable exponent spaces by linear integral operators.
J. Approx. Theory, 2017
Online pairwise learning algorithms with convex loss functions.
Inf. Sci., 2017
Generalization Error Bounds for Extreme Multi-class Classification.
CoRR, 2017
Online regularized learning with pairwise loss functions.
Adv. Comput. Math., 2017
2016
Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression.
IEEE Trans. Signal Process., 2016
Online Pairwise Learning Algorithms.
Neural Comput., 2016
Iterative Regularization for Learning with Convex Loss Functions.
J. Mach. Learn. Res., 2016
Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes.
J. Mach. Learn. Res., 2016
On the robustness of regularized pairwise learning methods based on kernels.
J. Complex., 2016
Fast Convergence of Online Pairwise Learning Algorithms.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016
2015
Learning theory of randomized Kaczmarz algorithm.
J. Mach. Learn. Res., 2015
Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition.
CoRR, 2015
Unregularized Online Learning Algorithms with General Loss Functions.
CoRR, 2015
Online Pairwise Learning Algorithms with Kernels.
CoRR, 2015
2014
Asymptotic Behaviour of Extinction Probability of Interacting Branching Collision Processes.
J. Appl. Probab., 2014
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm.
CoRR, 2014
2013
Learning theory approach to minimum error entropy criterion.
J. Mach. Learn. Res., 2013
An approximation theory approach to learning with <i>ℓ</i><sup>1</sup> regularization.
J. Approx. Theory, 2013
Concentration estimates for learning with unbounded sampling.
Adv. Comput. Math., 2013
2012
Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression.
J. Appl. Math., 2012
2011
Normal estimation on manifolds by gradient learning.
Numer. Linear Algebra Appl., 2011
Optimal learning rates for least squares regularized regression with unbounded sampling.
J. Complex., 2011
Learning with varying insensitive loss.
Appl. Math. Lett., 2011
2010
Hermite learning with gradient data.
J. Comput. Appl. Math., 2010
Moving least-square method in learning theory.
J. Approx. Theory, 2010
2009
Classification with Gaussians and Convex Loss.
J. Mach. Learn. Res., 2009
Online Learning with Samples Drawn from Non-identical Distributions.
J. Mach. Learn. Res., 2009
Gradient learning in a classification setting by gradient descent.
J. Approx. Theory, 2009
High order Parzen windows and randomized sampling.
Adv. Comput. Math., 2009
2008
Parzen windows for multi-class classification.
J. Complex., 2008
Learning with sample dependent hypothesis spaces.
Comput. Math. Appl., 2008
Learning and approximation by Gaussians on Riemannian manifolds.
Adv. Comput. Math., 2008
2007
Learnability of Gaussians with Flexible Variances.
J. Mach. Learn. Res., 2007
Multi-kernel regularized classifiers.
J. Complex., 2007
2006
Online Regularized Classification Algorithms.
IEEE Trans. Inf. Theory, 2006
Learning Coordinate Covariances via Gradients.
J. Mach. Learn. Res., 2006
Learning Rates of Least-Square Regularized Regression.
Found. Comput. Math., 2006
Approximation with polynomial kernels and SVM classifiers.
Adv. Comput. Math., 2006
2005
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming.
Neural Comput., 2005
2004
Support Vector Machine Soft Margin Classifiers: Error Analysis.
J. Mach. Learn. Res., 2004
Modeling Language Evolution.
Found. Comput. Math., 2004
2003
Capacity of reproducing kernel spaces in learning theory.
IEEE Trans. Inf. Theory, 2003
Properties of locally linearly independent refinable function vectors.
J. Approx. Theory, 2003
2002
Interpolatory orthogonal multiwavelets and refinable functions.
IEEE Trans. Signal Process., 2002
The covering number in learning theory.
J. Complex., 2002
Supports of Locally Linearly Independent <i>M</i>-Refinable Functions, Attractors of Iterated Function Systems and Tilings.
Adv. Comput. Math., 2002
2001
Self-Similar Lattice Tilings and Subdivision Schemes.
SIAM J. Math. Anal., 2001
2000
Convergence of Subdivision Schemes Associated with Nonnegative Masks.
SIAM J. Matrix Anal. Appl., 2000
1999
Smoothness of Multiple Refinable Functions and Multiple Wavelets.
SIAM J. Matrix Anal. Appl., 1999
System identification using wavelet neural networks.
Proceedings of the 5th European Control Conference, 1999
1998
Vector subdivision schemes and multiple wavelets.
Math. Comput., 1998
