Emilie Devijver

Office 319, Bâtiment IMAG
700 Av. Centrale
38058 Saint Martin d’Hères, France
Tel: +33 (0) 4 57 42 15 79
Mail: emilie.devijver at

Presentation           Publications        Teaching


Publications in journal
  • Survey and Evaluation of Causal Discovery Methods for Time Series , C.K. Assaad, E. Devijver and E. Gaussier, J. Artif. Int. Res. 73 (2022), version journal.
  • Semi-supervised Wrapper Feature Selection with a Genetic Algorithm, V. Feofanov, E. Devijver and M.-R. Amini, Applied Intelligence (2022), version journal.
  • Mixed effects modeling with warping for functional data using B-spline, G. Claeskens, E. Devijver and I. Gijbels, Electron. J. Statist. 15 (2) 5245 - 5282, 2021, version journal.
  • Prediction of the NASH through penalized mixture of logistic regression models, M. Morvan, E. Devijver, M. Giacofci, and V. Monbet, Annals of Applied Statistics, 15(2): 952-970, 2021, version journal.
  • Prediction regions through inverse regression, avec Emeline Perthame, Journal of Machine Learning Research, 2020, version journal.
  • Discussion sur "Pénalités minimales et heuristique de pente" par Sylvain Arlot, E. Devijver, Journal de la Société Française de Statistique, 160(3), 119:120.
  • Clustering electricity consumers using high-dimensional regression mixture models, avec Yannig Goude et Jean-Michel Poggi, Applied Stochastic Models in Business and Industry, 2019. doi:10.1002/asmb.2453.
  • Joint rank and variable selection for parsimonious estimation in high-dimensional finite mixture regression model, Emilie Devijver, JMVA, volume 157 (2017), 1-13, version journal, version ArXiv.
  • Block-diagonal covariance selection for high-dimensional Gaussian graphical models, Emilie Devijver et Mélina Gallopin, JASA (2016), version journal, version ArXiv.
  • Model-based clustering for high-dimensional data. Application to functional data, Emilie Devijver, ADAC, Volume 11 Issue 2 (2017), 243-279, version journal, version ArXiv.
  • Finite mixture regression: a sparse variable selection by model selection for clustering, Emilie Devijver, Electron. J. Statist. Volume 9, Number 2 (2015), 2642-2674, version journal, version ArXiv.
  • An l1-oracle inequality for the Lasso in finite mixture of multivariate Gaussian regression models, Emilie Devijver, ESAIM: PS 19 (2015) 649-670, version journal, version ArXiv.
Publications in peer-reviewed conference
  • Causal Discovery of Extended Summary Graphs in Time Series, C.K. Assaad, E. Devijver and E. Gaussier, UAI (2022) (in press)
  • A Mixed Noise and constraint-based Approach to Causal Inference in Time Series, K. Assaad, E. Devijver and E. Gaussier, In: Oliver N., Pérez-Cruz F., Kramer S., Read J., Lozano J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science, vol 12975. Springer, Cham, version conference
  • Smooth And Consistent Probabilistic Regression Trees, S. Alkhoury, E. Devijver, M. Clausel, M. Tami, E. Gaussier, G. Oppenheim, Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
  • Transductive Bounds for the Multi-class Majority Vote Classifier, Vasilii Feofanov, Emilie Devijver et Massih-Reza Amini, AAAI 2019
Publications in applied journal
  • Unsupervised topological learning for identification of atomic structures, S. Becker, E. Devijver, R. Molinier, N. Jakse, Physical Review E 105 (4) (2022)
  • Unsupervised topological learning approach of crystal nucleation, S. Becker, E. Devijver, R. Molinier, N. Jakse, Scientific Reports 12 (1), 1-9 (2022)
  • Glass-forming ability of elemental zirconium, S. Becker, E. Devijver, R. Molinier, and N. Jakse, Physical review B (2020), version journal
Publications in workshop
  • Unsupervised topological learning approach of crystal nucleation in pure Tantalum, S. Becker, E. Devijver, R. Molinier, and N. Jakse, Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)
  • Scaling Causal Inference in Additive Noise Models, K. Assaad, E. Devijver, E. Gaussier, A. Ait-Bachir, The 2019 ACM SIGKDD Workshop on Causal Discovery.
  • R code SelMix, with Benjamin Auder and Benjamin Goehry, available on the CRAN.
  • R code shock, with Mélina Gallopin, available on the CRAN .
  • R code warpMix, with Gerda Claeskens and Irène Gijbels, available on the CRAN .
  • Method BLLiM from R package xLLiM, with Emeline Perthame and Mélina Gallopin, available on the CRAN .