We introduce the nested stochastic block model (NSBM) to cluster a colle...
A spatially regularized Gaussian mixture model, LapGM, is proposed for t...
Kernel ridge regression (KRR) has recently attracted renewed interest du...
We introduce and study the neighbourhood lattice decomposition of a
dist...
We consider the task of learning causal structures from data stored on
m...
We propose a goodness-of-fit test for degree-corrected stochastic block
...
We study the concentration of random kernel matrices around their mean. ...
Knowing when a graphical model is perfect to a distribution is essential...
Kernel ridge regression (KRR) is a well-known and popular nonparametric
...
We consider the problem of identifying the source of an epidemic, spread...
Bayesian networks are a class of popular graphical models that encode ca...
Multiplex networks have become increasingly more prevalent in many field...
Leading methods for support recovery in high-dimensional regression, suc...
We propose an exact slice sampler for Hierarchical Dirichlet process (HD...
We consider the problem of estimating the parameters of a multivariate
B...
We consider the problem of bipartite community detection in networks, or...
We consider the analysis of spectral clustering algorithms for community...
We define and study partial correlation graphs (PCGs) with variables in ...
Community detection or clustering is a fundamental task in the analysis ...
We study a family of regularized score-based estimators for learning the...
The stochastic block model (SBM) is a popular tool for community detecti...
Many algorithms have been proposed for fitting network models with
commu...
We consider the sampling problem for functional PCA (fPCA), where the
si...
We consider a class of operator-induced norms, acting as finite-dimensio...