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ORCIDEmilie Devijver
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2020 – today
- 2024
[j10]Ashna Jose
, João Paulo Almeida de Mendonça, Emilie Devijver, Noël Jakse, Valérie Monbet, Roberta Poloni:
Regression tree-based active learning. Data Min. Knowl. Discov. 38(2): 420-460 (2024)- [j9]Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini:
Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data. J. Mach. Learn. Res. 25: 104:1-104:47 (2024) - [j8]Daria Bystrova, Charles K. Assaad, Julyan Arbel, Emilie Devijver, Éric Gaussier, Wilfried Thuiller:
Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms. Trans. Mach. Learn. Res. 2024 (2024) - [c8]Lei Zan, Charles K. Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir:
On the Fly Detection of Root Causes from Observed Data with Application to IT Systems. CIKM 2024: 5062-5069 - [c7]Lies Hadjadj, Emilie Devijver, Rémi Molinier, Massih-Reza Amini:
Efficient Initial Data Selection and Labeling for Multi-Class Classification Using Topological Analysis. ECAI 2024: 2677-2684 - [i14]Lei Zan, Charles K. Assaad, Emilie Devijver, Éric Gaussier:
On the Fly Detection of Root Causes from Observed Data with Application to IT Systems. CoRR abs/2402.06500 (2024) - [i13]Ashna Jose, Emilie Devijver, Massih-Reza Amini, Noël Jakse, Roberta Poloni:
Classification Tree-based Active Learning: A Wrapper Approach. CoRR abs/2404.09953 (2024) - [i12]Alexandre Seiller, Éric Gaussier, Emilie Devijver, Marianne Clausel, Sami Alkhoury:
Ensembles of Probabilistic Regression Trees. CoRR abs/2406.14033 (2024) - 2023
- [c6]Charles K. Assaad, Emilie Devijver, Éric Gaussier:
Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract). IJCAI 2023: 6839-6844 - [i11]Charles K. Assaad, Daria Bystrova, Julyan Arbel, Emilie Devijver, Éric Gaussier, Wilfried Thuiller:
Hybrids of Constraint-based and Noise-based Algorithms for Causal Discovery from Time Series. CoRR abs/2306.08765 (2023) - [i10]Ali Aït-Bachir, Charles K. Assaad, Christophe de Bignicourt, Emilie Devijver, Simon Ferreira, Éric Gaussier, Hosein Mohanna, Lei Zan:
Case Studies of Causal Discovery from IT Monitoring Time Series. CoRR abs/2307.15678 (2023) - [i9]Lies Hadjadj, Emilie Devijver, Rémi Molinier, Massih-Reza Amini:
Pool-Based Active Learning with Proper Topological Regions. CoRR abs/2310.01597 (2023) - [i8]Charles K. Assaad, Emilie Devijver, Éric Gaussier, Gregor Gössler, Anouar Meynaoui:
Identifiability of total effects from abstractions of time series causal graphs. CoRR abs/2310.14691 (2023) - 2022
- [j7]Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini:
Wrapper feature selection with partially labeled data. Appl. Intell. 52(11): 12316-12329 (2022) - [j6]Charles K. Assaad, Emilie Devijver, Éric Gaussier:
Entropy-Based Discovery of Summary Causal Graphs in Time Series. Entropy 24(8): 1156 (2022) - [j5]Lei Zan, Anouar Meynaoui, Charles K. Assaad, Emilie Devijver, Éric Gaussier:
A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test. Entropy 24(9): 1234 (2022) - [j4]Charles K. Assaad, Emilie Devijver, Éric Gaussier:
Survey and Evaluation of Causal Discovery Methods for Time Series. J. Artif. Intell. Res. 73: 767-819 (2022) - [c5]Charles K. Assaad, Emilie Devijver, Éric Gaussier:
Discovery of extended summary graphs in time series. UAI 2022: 96-106 - [i7]Massih-Reza Amini, Vasilii Feofanov, Loïc Pauletto, Emilie Devijver, Yury Maximov:
Self-Training: A Survey. CoRR abs/2202.12040 (2022) - [i6]Charles K. Assaad, Emilie Devijver, Éric Gaussier:
Inferring extended summary causal graphs from observational time series. CoRR abs/2205.09422 (2022) - 2021
- [c4]Charles K. Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir:
A Mixed Noise and Constraint-Based Approach to Causal Inference in Time Series. ECML/PKDD (1) 2021: 453-468 - [i5]Karim Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir:
Entropy-based Discovery of Summary Causal Graphs in Time Series. CoRR abs/2105.10381 (2021) - [i4]Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini:
Multi-class Probabilistic Bounds for Self-learning. CoRR abs/2109.14422 (2021) - 2020
- [j3]Emilie Devijver, Émeline Perthame:
Prediction regions through Inverse Regression. J. Mach. Learn. Res. 21: 113:1-113:24 (2020) - [c3]Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Éric Gaussier, Georges Oppenheim:
Smooth And Consistent Probabilistic Regression Trees. NeurIPS 2020
2010 – 2019
- 2019
- [c2]Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini:
Transductive Bounds for the Multi-Class Majority Vote Classifier. AAAI 2019: 3566-3573 - [c1]Charles K. Assaad, Emilie Devijver, Éric Gaussier, Ali Aït-Bachir:
Scaling Causal Inference in Additive Noise Models. CD@KDD 2019: 22-33 - [i3]Vasilii Feofanov, Massih-Reza Amini, Emilie Devijver:
Semi-supervised Wrapper Feature Selection with Imperfect Labels. CoRR abs/1911.04841 (2019) - 2018
- [i2]Myriam Tami, Marianne Clausel, Emilie Devijver, Adrien Dulac, Éric Gaussier, Stefan Janaqi, Mériam Chèbre:
Dealing with Uncertain Inputs in Regression Trees. CoRR abs/1810.11698 (2018) - 2017
- [j2]Emilie Devijver:
Model-based regression clustering for high-dimensional data: application to functional data. Adv. Data Anal. Classif. 11(2): 243-279 (2017) - [j1]Emilie Devijver:
Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model. J. Multivar. Anal. 157: 1-13 (2017) - 2015
- [i1]Emilie Devijver, Mélina Gallopin:
Block-diagonal covariance selection for high-dimensional Gaussian graphical models. CoRR abs/1511.04033 (2015)
Coauthor Index
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last updated on 2024-11-15 19:29 CET by the dblp team
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