Depression is a common mental health disorder that can cause consequenti...
K-means clustering is a widely used machine learning method for identify...
Data subsampling is widely used to speed up the training of large-scale
...
Diffusion-based generative graph models have been proven effective in
ge...
Variational Graph Autoencoders (VGAEs) are powerful models for unsupervi...
Learning to generate graphs is challenging as a graph is a set of pairwi...
Clustering is a widely deployed unsupervised learning tool. Model-based
...
Optimal transport (OT) offers a versatile framework to compare complex d...
Clustering is an important exploratory data analysis technique to group
...
This paper concerns the nonparametric estimation problem of the
distribu...
Semidefinite programming (SDP) is a powerful tool for tackling a wide ra...
Predicting future sensory states is crucial for learning agents such as
...
A graph generative model defines a distribution over graphs. One type of...
Motivated by statistical inference problems in high-dimensional time ser...
When formulated as an unsupervised learning problem, anomaly detection o...
This paper concerns the parameter estimation problem for the quadratic
p...
This paper introduces a dynamic panel SIR (DP-SIR) model to investigate ...
Principled nonparametric tests for regression curvature in R^d
are often...
This note provides the Stein equation for weighted sums of independent
χ...
Irregular functional data in which densely sampled curves are observed o...
We determine the cutoff value on separation of cluster centers for exact...
This paper is concerned with the estimation of time-varying networks for...
Deep Learning has been widely applied in the area of image processing an...
We consider the problem of change point detection for high-dimensional
d...
We introduce the diffusion K-means clustering method on Riemannian
subm...
We study the problem of distributional approximations to high-dimensiona...
We derive a dimensional-free Hanson-Wright inequality for quadratic form...
In traditional networks, interfaces of network nodes are duplex. But,
em...
In millimeter massive MIMO systems, hybrid beamforming algorithms with f...
This work deals with the optimization of computer programs targeting Gra...
This paper studies inference for the mean vector of a high-dimensional
U...
Cumulative sum (CUSUM) statistics are widely used in the change point
in...