60 citations · 266 across the 46 of their papers we have counts for
8 papers · 1 filter
Deep Neural Network Classifier for Multi-dimensional Functional Data
Shuoyang Wang, Guanqun Cao, Zuofeng Shang
We propose a new approach, called as functional deep neural network (FDNN), for classifying multi-dimensional functional data. Specifically, a deep neural network is trained based…
Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
Kexuan Li, Fangfang Wang, Ruiqi Liu +2
Ordinary differential equations (ODEs) are widely used to model complex dynamics that arises in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated…
Online Statistical Inference for Parameters Estimation with Linear-Equality Constraints
Ruiqi Liu, Mingao Yuan, Zuofeng Shang
Stochastic gradient descent (SGD) and projected stochastic gradient descent (PSGD) are scalable algorithms to compute model parameters in unconstrained and constrained optimization…
Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory
Ruiqi Liu, Ganggang Xu, Zuofeng Shang
When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an attractive tool for classification…
Heterogeneous Dense Subhypergraph Detection
Mingao Yuan, Zuofeng Shang
We study the problem of testing the existence of a heterogeneous dense subhypergraph. The null hypothesis corresponds to a heterogeneous Erdös-Rényi uniform random hypergraph and t…
Estimation of the Mean Function of Functional Data via Deep Neural Networks
Shuoyang Wang, Guanqun Cao, Zuofeng Shang
In this work, we propose a deep neural network method to perform nonparametric regression for functional data. The proposed estimators are based on sparsely connected deep neural n…
Sharp Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting
Tianyang Hu, Zuofeng Shang, Guang Cheng
Classifiers built with neural networks handle large-scale high dimensional data, such as facial images from computer vision, extremely well while traditional statistical methods of…
Distributed Generalized Cross-Validation for Divide-and-Conquer Kernel Ridge Regression and its Asymptotic Optimality
Ganggang Xu, Zuofeng Shang, Guang Cheng
Tuning parameter selection is of critical importance for kernel ridge regression. To this date, data driven tuning method for divide-and-conquer kernel ridge regression (d-KRR) has…