21 citations · 30 across the 5 of their papers we have counts for
5 papers
Efficient preconditioned stochastic gradient descent for estimation in latent variable models
Charlotte Baey, Maud Delattre, Estelle Kuhn +2
Latent variable models are powerful tools for modeling complex phenomena involving in particular partially observed data, unobserved variables or underlying complex unknown structu…
Parametrization Cookbook: A set of Bijective Parametrizations for using Machine Learning methods in Statistical Inference
Jean-Benoist Leger
We present in this paper a way to transform a constrained statistical inference problem into an unconstrained one in order to be able to use modern computational methods, such as t…
Learning from missing data with the Latent Block Model
Gabriel Frisch, Jean-Benoist Léger, Yves Grandvalet
Missing data can be informative. Ignoring this information can lead to misleading conclusions when the data model does not allow information to be extracted from the missing data.…
Blockmodels: A R-package for estimating in Latent Block Model and Stochastic Block Model, with various probability functions, with or without covariates
Jean-Benoist Leger
Analysis of the topology of a graph, regular or bipartite one, can be done by clustering for regular ones or co-clustering for bipartite ones. The Stochastic Block Model and the La…
Wmixnet: Software for Clustering the Nodes of Binary and Valued Graphs using the Stochastic Block Model
Jean-Benoist Leger
Clustering the nodes of a graph allows the analysis of the topology of a network. The stochastic block model is a clustering method based on a probabilistic model. Initially develo…