5 citations · 7 across the 5 of their papers we have counts for
6 papers
Predicting Spin-Crossover Behavior in Metal-Organic Frameworks from Limited and Noisy Data Using Quantile Active Learning
Ashna Jose, Emilie Devijver, Martin Uhrin +2
Spin-crossover (SCO) metal-organic frameworks (MOFs) hold great promise for sensing, spintronics, and gas-related applications, however, only a small number of SCO-active examples…
Mixture of segmentation for heterogeneous functional data
Vincent Brault, Émilie Devijver, Charlotte Laclau
In this paper we consider functional data with heterogeneity in time and in population. We propose a mixture model with segmentation of time to represent this heterogeneity while k…
Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model
Emilie Devijver
We study a dimensionality reduction technique for finite mixtures of high-dimensional multivariate response regression models. Both the dimension of the response and the number of…
An \ell_1-oracle inequality for the Lasso in finite mixture of multivariate Gaussian regression models
Emilie Devijver
We consider a multivariate finite mixture of Gaussian regression models for high-dimensional data, where the number of covariates and the size of the response may be much larger th…
Finite mixture regression: A sparse variable selection by model selection for clustering
Emilie Devijver
We consider a finite mixture of Gaussian regression model for high- dimensional data, where the number of covariates may be much larger than the sample size. We propose to estimate…
Model-based regression clustering for high-dimensional data. Application to functional data
Emilie Devijver
Finite mixture regression models are useful for modeling the relationship between response and predictors, arising from different subpopulations. In this article, we study high-dim…