Block-diagonal covariance selection for high-dimensional Gaussian graphical models. - Emilie Devijver; Mélina Gallopin (CoRR, 2015)

Model-based regression clustering for high-dimensional data: application to functional data. - Emilie Devijver (Adv. Data Anal. Classif., 2017)

Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model. - Emilie Devijver (J. Multivar. Anal., 2017)

Dealing with Uncertain Inputs in Regression Trees. - Myriam Tami; Marianne Clausel; Emilie Devijver; Adrien Dulac; Éric Gaussier; Stefan Janaqi; Mériam Chèbre (CoRR, 2018)

Transductive Bounds for the Multi-Class Majority Vote Classifier. - Vasilii Feofanov; Emilie Devijver; Massih-Reza Amini (AAAI, 2019)

Scaling Causal Inference in Additive Noise Models. - Charles K. Assaad; Emilie Devijver; Éric Gaussier; Ali Aït-Bachir (CD@KDD, 2019)

Semi-supervised Wrapper Feature Selection with Imperfect Labels. - Vasilii Feofanov; Massih-Reza Amini; Emilie Devijver (CoRR, 2019)

Smooth And Consistent Probabilistic Regression Trees. - Sami Alkhoury; Emilie Devijver; Marianne Clausel; Myriam Tami; Éric Gaussier; Georges Oppenheim (NeurIPS, 2020)

Prediction regions through Inverse Regression. - Emilie Devijver; Émeline Perthame (J. Mach. Learn. Res., 2020)

A Mixed Noise and Constraint-Based Approach to Causal Inference in Time Series. - Charles K. Assaad; Emilie Devijver; Éric Gaussier; Ali Aït-Bachir (ECML/PKDD (1), 2021)

Multi-class Probabilistic Bounds for Self-learning. - Vasilii Feofanov; Emilie Devijver; Massih-Reza Amini (CoRR, 2021)

Discovery of extended summary graphs in time series. - Charles K. Assaad; Emilie Devijver; Éric Gaussier (UAI, 2022)

Wrapper feature selection with partially labeled data. - Vasilii Feofanov; Emilie Devijver; Massih-Reza Amini (Appl. Intell., 2022)

Self-Training: A Survey. - Massih-Reza Amini; Vasilii Feofanov; Loïc Pauletto; Emilie Devijver; Yury Maximov (CoRR, 2022)

Inferring extended summary causal graphs from observational time series. - Charles K. Assaad; Emilie Devijver; Éric Gaussier (CoRR, 2022)

Entropy-Based Discovery of Summary Causal Graphs in Time Series. - Charles K. Assaad; Emilie Devijver; Éric Gaussier (Entropy, 2022)

A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test. - Lei Zan; Anouar Meynaoui; Charles K. Assaad; Emilie Devijver; Éric Gaussier (Entropy, 2022)

Survey and Evaluation of Causal Discovery Methods for Time Series. - Charles K. Assaad; Emilie Devijver; Éric Gaussier (J. Artif. Intell. Res., 2022)

Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract). - Charles K. Assaad; Emilie Devijver; Éric Gaussier (IJCAI, 2023)

Hybrids of Constraint-based and Noise-based Algorithms for Causal Discovery from Time Series. - Charles K. Assaad; Daria Bystrova; Julyan Arbel; Emilie Devijver; Éric Gaussier; Wilfried Thuiller (CoRR, 2023)

Case Studies of Causal Discovery from IT Monitoring Time Series. - Ali Aït-Bachir; Charles K. Assaad; Christophe de Bignicourt; Emilie Devijver; Simon Ferreira; Éric Gaussier; Hosein Mohanna; Lei Zan (CoRR, 2023)

Pool-Based Active Learning with Proper Topological Regions. - Lies Hadjadj; Emilie Devijver; Rémi Molinier; Massih-Reza Amini (CoRR, 2023)

On the Fly Detection of Root Causes from Observed Data with Application to IT Systems. - Lei Zan; Charles K. Assaad; Emilie Devijver; Éric Gaussier; Ali Aït-Bachir (CIKM, 2024)

Efficient Initial Data Selection and Labeling for Multi-Class Classification Using Topological Analysis. - Lies Hadjadj; Emilie Devijver; Rémi Molinier; Massih-Reza Amini (ECAI, 2024)

Identifiability of total effects from abstractions of time series causal graphs. - Charles K. Assaad; Emilie Devijver; Éric Gaussier; Gregor Goessler; Anouar Meynaoui (UAI, 2024)

On the Fly Detection of Root Causes from Observed Data with Application to IT Systems. - Lei Zan; Charles K. Assaad; Emilie Devijver; Éric Gaussier (CoRR, 2024)

Classification Tree-based Active Learning: A Wrapper Approach. - Ashna Jose; Emilie Devijver; Massih-Reza Amini; Noël Jakse; Roberta Poloni (CoRR, 2024)

Ensembles of Probabilistic Regression Trees. - Alexandre Seiller; Éric Gaussier; Emilie Devijver; Marianne Clausel; Sami Alkhoury (CoRR, 2024)

Regression tree-based active learning. - Ashna Jose; João Paulo Almeida de Mendonça; Emilie Devijver; Noël Jakse; Valérie Monbet; Roberta Poloni (Data Min. Knowl. Discov., 2024)

Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data. - Vasilii Feofanov; Emilie Devijver; Massih-Reza Amini (J. Mach. Learn. Res., 2024)

Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms. - Daria Bystrova; Charles K. Assaad; Julyan Arbel; Emilie Devijver; Éric Gaussier; Wilfried Thuiller (Trans. Mach. Learn. Res., 2024)

Complete Characterization for Adjustment in Summary Causal Graphs of Time Series. - Clément Yvernes; Emilie Devijver; Éric Gaussier (UAI, 2025)

Self-training: A survey. - Massih-Reza Amini; Vasilii Feofanov; Loïc Pauletto; Lies Hadjadj; Emilie Devijver; Yury Maximov (Neurocomputing, 2025)

Identifiability by common backdoor in summary causal graphs of time series. - Clément Yvernes; Charles K. Assaad; Emilie Devijver; Éric Gaussier (CoRR, 2025)

Identifiability in Causal Abstractions: A Hierarchy of Criteria. - Clément Yvernes; Emilie Devijver; Marianne Clausel; Éric Gaussier (CoRR, 2025)