Data silos, mainly caused by privacy and interoperability, significantly...
This paper focuses on approximation and learning performance analysis fo...
This paper studies the performance of deep convolutional neural networks...
This paper proposes a sketching strategy based on spherical designs, whi...
In recent years, large amounts of electronic health records (EHRs) conce...
This paper proposes a distributed weighted regularized least squares
alg...
In this paper, we study the generalization performance of global minima ...
This paper focuses on learning rate analysis of Nyström regularization
w...
Compared with avid research activities of deep convolutional neural netw...
Developing efficient kernel methods for regression is very popular in th...
Data sites selected from modeling high-dimensional problems often appear...
Deep learning is recognized to be capable of discovering deep features f...
Boosting is a well-known method for improving the accuracy of weak learn...
This paper focuses on generalization performance analysis for distribute...
This paper focuses on learning rate analysis of distributed kernel ridge...
In this paper, we propose an adaptive stopping rule for kernel-based gra...
The great success of deep learning poses urgent challenges for understan...
In the era of big data, it is highly desired to develop efficient machin...
Problems in astrophysics, space weather research and geophysics usually ...
Based on the tree architecture, the objective of this paper is to design...
Efficient training of deep neural networks (DNNs) is a challenge due to ...
This paper considers the power of deep neural networks (deep nets for sh...
Along with the rapid development of deep learning in practice, the
theor...
The subject of deep learning has recently attracted users of machine lea...
This paper aims at refined error analysis for binary classification usin...
We study distributed learning with the least squares regularization sche...