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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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08/07/2023
Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks
This paper focuses on approximation and learning performance analysis fo...
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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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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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02/21/2023
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes
In recent years, large amounts of electronic health records (EHRs) conce...
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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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11/13/2021
Nyström Regularization for Time Series Forecasting
This paper focuses on learning rate analysis of Nyström regularization w...
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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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09/16/2020
Kernel-based L_2-Boosting with Structure Constraints
Developing efficient kernel methods for regression is very popular in th...
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09/03/2020
Kernel Interpolation of High Dimensional Scattered Data
Data sites selected from modeling high-dimensional problems often appear...
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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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04/01/2020
Fully-Corrective Gradient Boosting with Squared Hinge: Fast Learning Rates and Early Stopping
Boosting is a well-known method for improving the accuracy of weak learn...
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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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02/10/2020
Distributed Learning with Dependent Samples
This paper focuses on learning rate analysis of distributed kernel ridge...
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01/09/2020
Adaptive Stopping Rule for Kernel-based Gradient Descent Algorithms
In this paper, we propose an adaptive stopping rule for kernel-based gra...
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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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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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02/06/2019
A Convergence Analysis of Nonlinearly Constrained ADMM in Deep Learning
Efficient training of deep neural networks (DNNs) is a challenge due to ...
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01/01/2019
Realizing data features by deep nets
This paper considers the power of deep neural networks (deep nets for sh...
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03/10/2018
Generalization and Expressivity for Deep Nets
Along with the rapid development of deep learning in practice, the theor...
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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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02/28/2017
Learning rates for classification with Gaussian kernels
This paper aims at refined error analysis for binary classification usin...
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08/11/2016
