6 citations · 17 across the 13 of their papers we have counts for
5 papers · 1 filter
A Bayesian framework with adaptive elastic nets for the inference of Gaussian graphical models
Roland B. Sogan, Tabea Rebafka, Fanny Villers
Estimating conditional independence graphs from high-dimensional Gaussian data is challenging because methods must detect relevant edges while rigorously controlling statistical er…
Low-Complexity and Consistent Graphon Estimation from Multiple Networks
Roland Boniface Sogan, Tabea Rebafka
Recovering the random graph model from an observed collection of networks is known to present significant challenges in the setting, where the networks do not share a common node s…
Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure
Valentin Kilian, Tabea Rebafka, Fanny Villers
For the problem of inferring a Gaussian graphical model (GGM), this work explores the application of a recent approach from the multiple testing literature for graph inference. The…
Self-Organizing Maps for Exploration of Partially Observed Data and Imputation of Missing Values
Sara Rejeb, Catherine Duveau, Tabea Rebafka
The self-organizing map is an unsupervised neural network which is widely used for data visualisation and clustering in the field of chemometrics. The classical Kohonen algorithm t…
A semiparametric extension of the stochastic block model for longitudinal networks
Catherine Matias, Tabea Rebafka, Fanny Villers
To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions b…