Publications in journal
- Self-Training: A Survey, Amini, M.R., Feofanov, V., Pauletto, L., Hadjadj, L., Devijver, E., Maximov, Y. (2024). Neurocomputing, version journal .
- Mixture of segmentation for heterogeneous functional data, Brault, V., Devijver, E., Laclau C. (2024). Electronic Journal of Statistics, version journal .
- On the Fly Detection of Root Causes from Observed Data with Application to IT Systems, K. Zan, A. Ait-Bachir, C. K. Assaad, E. Devijver, E. Gaussier (2024). CIKM, version conference .
- Efficient Initial Data Selection and Labeling for Multi-Class Classification Using Topological Analysis, L. Hadjadj, E. Devijver, R. Molinier, M.-R. Amini (2024). ECAI, version conference .
- Identifiability of total effects from abstractions of time series causal graphs, C.K. Assaad, E. Devijver, E. Gaussier, G. Goessler, A. Meynaoui (2024). UAI, version conference .
- Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms, D. Bystrova, C.K. Assaad, J. Arbel, E. Devijver, E. Gaussier, W. Thuillier (2024). TMLR, version journal .
- Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data,
V. Feofanov, E. Devijver, M.-R. Amini (2024). JMLR 25(104):1-47, version journal .
- Nonlinear network-based quantitative trait prediction from biological data}, M. Blein-Nicolas, E. Devijver, M. Gallopin, E. Perthame (2024). Journal of the Royal Statistical Society Series C: Applied Statistics, 13(3) 796–815, version journal .
- Regression tree-based active learning}, A. Jose, J. P. Almeida de Mendonça, E. Devijver, N. Jakse, V. Monbet and R. Poloni (2023). Data Mining and Knowledge Discovery and ECML PKDD, version conference .
- A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test}, L. Zan, A. Meynaoui, C.K. Assaad, E. Devijver and E. Gaussier (2022). Entropy, 24(9), version 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.
- Wrapper Feature Selection with partially labeled data, V. Feofanov, E. Devijver and M.-R. Amini, Applied Intelligence (2022), version journal.
- Discovery of Extended Summary Graphs in Time Series, C.K. Assaad, E. Devijver and E. Gaussier, UAI (2022), version conference.
- 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
- 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.
- 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
- 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 applied journal
- Feature Selection for High-Dimensional Neural Network Potentials with the Adaptive Group Lasso, J. Sandberg, E. Devijver, N. Jakse, T. Voigtmann (2024). Machine Learning: Science and Technology.
- Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning, A. Jose, E. Devijver, N. Jakse, R. Poloni, (2024). J. Am. Chem. Soc. 146, 6134.
- An Artificial Neural Network-based Density Functional Approach for Adiabatic Energy Differences in Transition Metal Complexes, J.P. Almeida de Mendonca, A. L. Mariano, E. Devijver, N. Jakse, R. Poloni (2023). J. Chem. Theory Comput. 19, 7555.
- 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
Implementation
- Python code sfor root cause analysis, with Lei Zan, Charles K. Assaad and Eric Gaussier, available on Git .
- Python code for the state-of-the-art in causal discovery for time series, with Charles K. Assaad and Eric Gaussier, available on Git .
- Python code SLA, for semi-supervised learning in classification, with Vasilii Feofanov and Massih-Reza Amini, available on Git , and it extension to feature selection here .
- Python code PTR, for active learning in classification, with Lies Hadjadj, Rémi Molinier, and Massih-Reza Amini, available on Git .
- 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 .
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