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09/08/2023
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos
Data silos, mainly caused by privacy and interoperability, significantly...
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08/18/2023
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
Physics-informed neural networks (PINNs) have been demonstrated to be ef...
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07/31/2023
Classification with Deep Neural Networks and Logistic Loss
Deep neural networks (DNNs) trained with the logistic loss (i.e., the cr...
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07/30/2023
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning
This paper studies the performance of deep convolutional neural networks...
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07/24/2023
Rates of Approximation by ReLU Shallow Neural Networks
Neural networks activated by the rectified linear unit (ReLU) play a cen...
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07/07/2023
Learning Theory of Distribution Regression with Neural Networks
In this paper, we aim at establishing an approximation theory and a lear...
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06/14/2023
Nonparametric regression using over-parameterized shallow ReLU neural networks
It is shown that over-parameterized neural networks can achieve minimax ...
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05/31/2023
Optimal Estimates for Pairwise Learning with Deep ReLU Networks
Pairwise learning refers to learning tasks where a loss takes a pair of ...
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05/26/2023
Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
Recently, significant progress has been made in understanding the genera...
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05/12/2023
Distributed Gradient Descent for Functional Learning
In recent years, different types of distributed learning schemes have re...
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04/10/2023
Approximation of Nonlinear Functionals Using Deep ReLU Networks
In recent years, functional neural networks have been proposed and studi...
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04/04/2023
Optimal rates of approximation by shallow ReLU^k neural networks and applications to nonparametric regression
We study the approximation capacity of some variation spaces correspondi...
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03/08/2023
Sketching with Spherical Designs for Noisy Data Fitting on Spheres
This paper proposes a sketching strategy based on spherical designs, whi...
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02/24/2023
Generalization Analysis for Contrastive Representation Learning
Recently, contrastive learning has found impressive success in advancing...
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10/19/2022
A new activation for neural networks and its approximation
Deep learning with deep neural networks (DNNs) has attracted tremendous ...
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10/14/2022
Approximation analysis of CNNs from feature extraction view
Deep learning based on deep neural networks has been very successful in ...
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09/16/2022
Stability and Generalization for Markov Chain Stochastic Gradient Methods
Recently there is a large amount of work devoted to the study of Markov ...
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09/09/2022
Differentially Private Stochastic Gradient Descent with Low-Noise
In this paper, by introducing a low-noise condition, we study privacy an...
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02/24/2022
Attention Enables Zero Approximation Error
Deep learning models have been widely applied in various aspects of dail...
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12/05/2021
Radial Basis Function Approximation with Distributively Stored Data on Spheres
This paper proposes a distributed weighted regularized least squares alg...
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11/28/2021
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets
In this paper, we study the generalization performance of global minima ...
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07/02/2021
Theory of Deep Convolutional Neural Networks III: Approximating Radial Functions
We consider a family of deep neural networks consisting of two groups of...
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06/23/2021
Universal Consistency of Deep Convolutional Neural Networks
Compared with avid research activities of deep convolutional neural netw...
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04/21/2021
Robust Kernel-based Distribution Regression
Regularization schemes for regression have been widely studied in learni...
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07/28/2020
Theory of Deep Convolutional Neural Networks II: Spherical Analysis
Deep learning based on deep neural networks of various structures and ar...
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04/01/2020
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization
Deep learning is recognized to be capable of discovering deep features f...
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03/27/2020
Distributed Kernel Ridge Regression with Communications
This paper focuses on generalization performance analysis for distribute...
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12/16/2019
Realization of spatial sparseness by deep ReLU nets with massive data
The great success of deep learning poses urgent challenges for understan...
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12/03/2019
Towards Understanding the Spectral Bias of Deep Learning
An intriguing phenomenon observed during training neural networks is the...
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11/24/2019
Fast Polynomial Kernel Classification for Massive Data
In the era of big data, it is highly desired to develop efficient machin...
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10/06/2019
Distributed filtered hyperinterpolation for noisy data on the sphere
Problems in astrophysics, space weather research and geophysics usually ...
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04/03/2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning
Based on the tree architecture, the objective of this paper is to design...
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05/28/2018
Universality of Deep Convolutional Neural Networks
Deep learning has been widely applied and brought breakthroughs in speec...
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03/09/2018
Construction of neural networks for realization of localized deep learning
The subject of deep learning has recently attracted users of machine lea...
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02/18/2018
Convergence of Online Mirror Descent Algorithms
In this paper we consider online mirror descent (OMD) algorithms, a clas...
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09/22/2017
Total stability of kernel methods
Regularized empirical risk minimization using kernels and their correspo...
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08/11/2016
Distributed learning with regularized least squares
We study distributed learning with the least squares regularization sche...
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10/12/2015
On the Robustness of Regularized Pairwise Learning Methods Based on Kernels
Regularized empirical risk minimization including support vector machine...
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03/31/2015
Iterative Regularization for Learning with Convex Loss Functions
We consider the problem of supervised learning with convex loss function...
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03/10/2015
Minimax Optimal Rates of Estimation in High Dimensional Additive Models: Universal Phase Transition
We establish minimax optimal rates of convergence for estimation in a hi...
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03/02/2015
Unregularized Online Learning Algorithms with General Loss Functions
In this paper, we consider unregularized online learning algorithms in a...
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02/25/2015
Online Pairwise Learning Algorithms with Kernels
Pairwise learning usually refers to a learning task which involves a los...
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12/17/2014
Consistency Analysis of an Empirical Minimum Error Entropy Algorithm
In this paper we study the consistency of an empirical minimum error ent...
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05/14/2014
Learning rates for the risk of kernel based quantile regression estimators in additive models
Additive models play an important role in semiparametric statistics. Thi...
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08/03/2012