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ORCIDJulie Josse
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2020 – today
- 2024
[j17]Aude Sportisse, Matthieu Marbac, Fabien Laporte, Gilles Celeux, Claire Boyer, Julie Josse, Christophe Biernacki:
Model-based clustering with missing not at random data. Stat. Comput. 34(4): 135 (2024)- [c15]Clément Bénard, Jeffrey Näf, Julie Josse:
MMD-based Variable Importance for Distributional Random Forest. AISTATS 2024: 1324-1332 - [c14]Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang:
Positivity-free Policy Learning with Observational Data. AISTATS 2024: 1918-1926 - 2023
- [c13]Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano:
Conformal Prediction with Missing Values. ICML 2023: 40578-40604 - [i15]Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano:
Conformal Prediction with Missing Values. CoRR abs/2306.02732 (2023) - [i14]Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang:
Positivity-free Policy Learning with Observational Data. CoRR abs/2310.06969 (2023) - [i13]Clément Bénard, Jeffrey Näf, Julie Josse:
MMD-based Variable Importance for Distributional Random Forest. CoRR abs/2310.12115 (2023) - 2022
- [j16]Wei Jiang
, Malgorzata Bogdan, Julie Josse, Szymon Majewski, Blazej Miasojedow, Veronika Rocková:
Adaptive Bayesian SLOPE: Model Selection With Incomplete Data. J. Comput. Graph. Stat. 31(1): 113-137 (2022) - [j15]Imke Mayer, Aude Sportisse, Julie Josse, Nicholas J. Tierney, Nathalie Vialaneix:
R-miss-tastic: a unified platform for missing values methods and workflows. R J. 14(2): 245-267 (2022) - [c12]Margaux Zaffran, Olivier Féron, Yannig Goude, Julie Josse, Aymeric Dieuleveut:
Adaptive Conformal Predictions for Time Series. ICML 2022: 25834-25866 - [i12]Margaux Zaffran, Aymeric Dieuleveut, Olivier Féron, Yannig Goude, Julie Josse:
Adaptive Conformal Predictions for Time Series. CoRR abs/2202.07282 (2022) - [i11]Alexandre Perez-Lebel, Gaël Varoquaux, Marine Le Morvan, Julie Josse, Jean-Baptiste Poline:
Benchmarking missing-values approaches for predictive models on health databases. CoRR abs/2202.10580 (2022) - 2021
- [c11]Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux:
What's a good imputation to predict with missing values? NeurIPS 2021: 11530-11540 - [i10]Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux:
What's a good imputation to predict with missing values? CoRR abs/2106.00311 (2021) - [i9]Aude Sportisse, Christophe Biernacki, Claire Boyer, Julie Josse, Matthieu Marbac Lourdelle, Gilles Celeux, Fabien Laporte:
Model-based Clustering with Missing Not At Random Data. CoRR abs/2112.10425 (2021) - 2020
- [j14]Wei Jiang, Julie Josse, Marc Lavielle, TraumaBase Group:
Logistic regression with missing covariates - Parameter estimation, model selection and prediction within a joint-modeling framework. Comput. Stat. Data Anal. 145: 106907 (2020) - [j13]Aude Sportisse, Claire Boyer, Julie Josse:
Imputation and low-rank estimation with Missing Not At Random data. Stat. Comput. 30(6): 1629-1643 (2020) - [c10]Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux:
Linear predictor on linearly-generated data with missing values: non consistency and solutions. AISTATS 2020: 3165-3174 - [c9]Lucas Martin, Julie Josse, Bertrand Thirion:
Multivariate Analysis is Sufficient for Lesion-Behaviour Mapping. BrainLes@MICCAI (1) 2020: 92-100 - [c8]Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi:
Missing Data Imputation using Optimal Transport. ICML 2020: 7130-7140 - [c7]Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux:
NeuMiss networks: differentiable programming for supervised learning with missing values. NeurIPS 2020 - [c6]Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse:
Debiasing Averaged Stochastic Gradient Descent to handle missing values. NeurIPS 2020 - [c5]Aude Sportisse, Claire Boyer, Julie Josse:
Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data. NeurIPS 2020 - [i8]Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux:
Linear predictor on linearly-generated data with missing values: non consistency and solutions. CoRR abs/2002.00658 (2020) - [i7]Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi:
Missing Data Imputation using Optimal Transport. CoRR abs/2002.03860 (2020) - [i6]Imke Mayer, Julie Josse, Félix Raimundo, Jean-Philippe Vert:
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models. CoRR abs/2002.10837 (2020) - [i5]Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux:
Neumann networks: differential programming for supervised learning with missing values. CoRR abs/2007.01627 (2020)
2010 – 2019
- 2019
- [j12]Borja Seijo-Pardo, Amparo Alonso-Betanzos, Kristin P. Bennett, Verónica Bolón-Canedo, Julie Josse, Mehreen Saeed, Isabelle Guyon:
Biases in feature selection with missing data. Neurocomputing 342: 97-112 (2019) - [j11]Geneviève Robin, Julie Josse, Eric Moulines, Sylvain Sardy:
Low-rank model with covariates for count data with missing values. J. Multivar. Anal. 173: 416-434 (2019) - [i4]Julie Josse, Nicolas Prost, Erwan Scornet, Gaël Varoquaux:
On the consistency of supervised learning with missing values. CoRR abs/1902.06931 (2019) - 2018
- [c4]Borja Seijo-Pardo, Amparo Alonso-Betanzos, Kristin P. Bennett, Verónica Bolón-Canedo, Isabelle Guyon, Julie Josse, Mehreen Saeed:
Analysis of imputation bias for feature selection with missing data. ESANN 2018 - [c3]Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines:
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames. NeurIPS 2018: 5501-5511 - [i3]Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines:
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames. CoRR abs/1812.08398 (2018) - [i2]Aude Sportisse, Claire Boyer, Julie Josse:
Imputation and low-rank estimation with Missing Non At Random data. CoRR abs/1812.11409 (2018) - 2017
- [j10]William Fithian, Julie Josse:
Multiple correspondence analysis and the multilogit bilinear model. J. Multivar. Anal. 157: 87-102 (2017) - [j9]Vincent Audigier, François Husson, Julie Josse:
MIMCA: multiple imputation for categorical variables with multiple correspondence analysis. Stat. Comput. 27(2): 501-518 (2017) - 2016
- [j8]Vincent Audigier, François Husson, Julie Josse:
A principal component method to impute missing values for mixed data. Adv. Data Anal. Classif. 10(1): 5-26 (2016) - [j7]Julie Josse, Stefan Wager:
Bootstrap-Based Regularization for Low-Rank Matrix Estimation. J. Mach. Learn. Res. 17: 124:1-124:29 (2016) - [j6]Julie Josse, Sylvain Sardy:
Adaptive shrinkage of singular values. Stat. Comput. 26(3): 715-724 (2016) - 2015
- [j5]Marie Verbanck, Julie Josse, François Husson:
Regularised PCA to denoise and visualise data. Stat. Comput. 25(2): 471-486 (2015) - [c2]Julie Josse, Stefan Wager:
Stable Autoencoding: A Flexible Framework for Regularized Low-rank Matrix Estimation. ICCS 2015: 2406 - 2014
- [i1]Julie Josse, Stefan Wager:
Stable Autoencoding: A Flexible Framework for Regularized Low-Rank Matrix Estimation. CoRR abs/1410.8275 (2014) - 2012
- [j4]Julie Josse, Marie Chavent, Benoit Liquet, François Husson:
Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis. J. Classif. 29(1): 91-116 (2012) - [j3]Julie Josse, François Husson:
Selecting the number of components in principal component analysis using cross-validation approximations. Comput. Stat. Data Anal. 56(6): 1869-1879 (2012) - 2011
- [j2]Julie Josse, Jérôme Pagès, François Husson:
Multiple imputation in principal component analysis. Adv. Data Anal. Classif. 5(3): 231-246 (2011)
2000 – 2009
- 2008
- [j1]Julie Josse, Jérôme Pagès, François Husson:
Testing the significance of the RV coefficient. Comput. Stat. Data Anal. 53(1): 82-91 (2008) - 2006
- [c1]Teddy Furon, Julie Josse, Sandrine Le Squin:
Some theoretical aspects of watermarking detection. Security, Steganography, and Watermarking of Multimedia Contents 2006: 60721G
Coauthor Index
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last updated on 2024-09-13 01:37 CEST by the dblp team
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