In 1-bit matrix completion, the aim is to estimate an underlying low-ran...
The growing prevalence of tensor data, or multiway arrays, in science an...
This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a
f...
Building on previous research of Chi and Chi (2022), the current paper
r...
Proximal Markov Chain Monte Carlo is a novel construct that lies at the
...
This paper deals with the grouped variable selection problem. A widely u...
We introduce a user-friendly computational framework for implementing ro...
Graph signal processing (GSP) is an important methodology for studying
a...
We address the problem of estimating smoothly varying baseline trends in...
Representation learning is typically applied to only one mode of a data
...
Convex clustering refers, for given {x_1, ..., x_n}⊂^p, to the minimizat...
Cluster analysis is a fundamental tool for pattern discovery of complex
...
Estimation in generalized linear models (GLM) is complicated by the pres...
We consider N-way data arrays and low-rank tensor factorizations where t...
In the biclustering problem, we seek to simultaneously group observation...
Clustering is a fundamental problem in many scientific applications. Sta...
The problem of minimizing a continuously differentiable convex function ...
We investigate a robust penalized logistic regression algorithm based on...