Factor analysis provides a canonical framework for imposing lower-dimens...
Branching processes are a class of continuous-time Markov chains (CTMCs)...
Viral deep-sequencing data play a crucial role toward understanding dise...
Throughout the course of an epidemic, the rate at which disease spreads
...
Stochastic versions of proximal methods have gained much attention in
st...
Constrained learning is prevalent in many statistical tasks. Recent work...
Recent progress in center-based clustering algorithms combats poor local...
A custom Wi-Fi and Bluetooth indoor contact tracing system is created to...
Intensive care occupancy is an important indicator of health care stress...
Stochastic epidemic models provide an interpretable probabilistic descri...
The principle of majorization-minimization (MM) provides a general frame...
We develop a stochastic epidemic model progressing over dynamic networks...
Multitype branching processes are ideal for studying the population dyna...
Recent advances in center-based clustering continue to improve upon the
...
This paper addresses the task of estimating a covariance matrix under a
...
We introduce a novel class of stochastic blockmodel for multilayer weigh...
Kernel k-means clustering is a powerful tool for unsupervised learning o...
Convex clustering has recently garnered increasing interest due to its
a...
Clustering, a fundamental activity in unsupervised learning, is notoriou...
Despite its well-known shortcomings, k-means remains one of the most wid...
We propose a generative model and an inference scheme for epidemic proce...
One means of fitting functions to high-dimensional data is by providing
...
Deep learning has achieved impressive results on many problems. However,...
Automatic conflict detection has grown in relevance with the advent of
b...
Automatic conflict detection has grown in relevance with the advent of
b...
Estimation in generalized linear models (GLM) is complicated by the pres...
Variational inference algorithms have proven successful for Bayesian ana...