78 citations · 412 across the 48 of their papers we have counts for
13 papers · 1 filter
Learning Theory of Distribution Regression with Neural Networks
Zhongjie Shi, Zhan Yu, Ding-Xuan Zhou
In this paper, we aim at establishing an approximation theory and a learning theory of distribution regression via a fully connected neural network (FNN). In contrast to the classi…
Classification with Deep Neural Networks and Logistic Loss
Zihan Zhang, Lei Shi, Ding-Xuan Zhou
Deep neural networks (DNNs) trained with the logistic loss (i.e., the cross entropy loss) have made impressive advancements in various binary classification tasks. However, general…
Nonparametric regression using over-parameterized shallow ReLU neural networks
Yunfei Yang, Ding-Xuan Zhou
It is shown that over-parameterized neural networks can achieve minimax optimal rates of convergence (up to logarithmic factors) for learning functions from certain smooth function…
Fine-grained Analysis of Non-parametric Estimation for Pairwise Learning
Junyu Zhou, Shuo Huang, Han Feng +2
In this paper, we are concerned with the generalization performance of non-parametric estimation for pairwise learning. Most of the existing work requires the hypothesis space to b…
Distributed Gradient Descent for Functional Learning
Zhan Yu, Jun Fan, Zhongjie Shi +1
In recent years, different types of distributed and parallel learning schemes have received increasing attention for their strong advantages in handling large-scale data informatio…
Approximation of Nonlinear Functionals Using Deep ReLU Networks
Linhao Song, Jun Fan, Di-Rong Chen +1
In recent years, functional neural networks have been proposed and studied in order to approximate nonlinear continuous functionals defined on for integers …