Julie Josse

According to our database, Julie Josse authored at least 37 papers between 2006 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

MMD-based Variable Importance for Distributional Random Forest.

Clément Bénard, Jeffrey Näf, Julie Josse

CoRR, 2023

Positivity-free Policy Learning with Observational Data.

Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang

CoRR, 2023

Conformal Prediction with Missing Values.

Margaux Zaffran, Aymeric Dieuleveut, Julie Josse, Yaniv Romano

Proceedings of the International Conference on Machine Learning, 2023

2022

R-miss-tastic: a unified platform for missing values methods and workflows.

Imke Mayer, Aude Sportisse, Julie Josse, Nicholas J. Tierney, Nathalie Vialaneix

R J., 2022

Adaptive Bayesian SLOPE: Model Selection With Incomplete Data.

Wei Jiang, Malgorzata Bogdan, Julie Josse, Szymon Majewski, Blazej Miasojedow, Veronika Rocková

J. Comput. Graph. Stat., 2022

Benchmarking missing-values approaches for predictive models on health databases.

Alexandre Perez-Lebel, Gaël Varoquaux, Marine Le Morvan, Julie Josse, Jean-Baptiste Poline

CoRR, 2022

Adaptive Conformal Predictions for Time Series.

Margaux Zaffran, Olivier Féron, Yannig Goude, Julie Josse, Aymeric Dieuleveut

Proceedings of the International Conference on Machine Learning, 2022

2021

Model-based Clustering with Missing Not At Random Data.

Aude Sportisse, Christophe Biernacki, Claire Boyer, Julie Josse, Matthieu Marbac Lourdelle, Gilles Celeux, Fabien Laporte

CoRR, 2021

What's a good imputation to predict with missing values?

Marine Le Morvan, Julie Josse, Erwan Scornet, Gaël Varoquaux

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Imputation and low-rank estimation with Missing Not At Random data.

Aude Sportisse, Claire Boyer, Julie Josse

Stat. Comput., 2020

Logistic regression with missing covariates - Parameter estimation, model selection and prediction within a joint-modeling framework.

Wei Jiang, Julie Josse, Marc Lavielle, TraumaBase Group

Comput. Stat. Data Anal., 2020

Neumann networks: differential programming for supervised learning with missing values.

Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux

CoRR, 2020

MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models.

Imke Mayer, Julie Josse, Félix Raimundo, Jean-Philippe Vert

CoRR, 2020

Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data.

Aude Sportisse, Claire Boyer, Julie Josse

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Debiasing Averaged Stochastic Gradient Descent to handle missing values.

Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

NeuMiss networks: differentiable programming for supervised learning with missing values.

Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Missing Data Imputation using Optimal Transport.

Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi

Proceedings of the 37th International Conference on Machine Learning, 2020

Multivariate Analysis is Sufficient for Lesion-Behaviour Mapping.

Lucas Martin, Julie Josse, Bertrand Thirion

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

Linear predictor on linearly-generated data with missing values: non consistency and solutions.

Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, Gaël Varoquaux

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Low-rank model with covariates for count data with missing values.

Geneviève Robin, Julie Josse, Eric Moulines, Sylvain Sardy

J. Multivar. Anal., 2019

Biases in feature selection with missing data.

Borja Seijo-Pardo, Amparo Alonso-Betanzos, Kristin P. Bennett, Verónica Bolón-Canedo, Julie Josse, Mehreen Saeed, Isabelle Guyon

Neurocomputing, 2019

On the consistency of supervised learning with missing values.

Julie Josse, Nicolas Prost, Erwan Scornet, Gaël Varoquaux

CoRR, 2019

2018

Imputation and low-rank estimation with Missing Non At Random data.

Aude Sportisse, Claire Boyer, Julie Josse

CoRR, 2018

Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames.

Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Analysis of imputation bias for feature selection with missing data.

Borja Seijo-Pardo, Amparo Alonso-Betanzos, Kristin P. Bennett, Verónica Bolón-Canedo, Isabelle Guyon, Julie Josse, Mehreen Saeed

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017

MIMCA: multiple imputation for categorical variables with multiple correspondence analysis.

Vincent Audigier, François Husson, Julie Josse

Stat. Comput., 2017

Multiple correspondence analysis and the multilogit bilinear model.

William Fithian, Julie Josse

J. Multivar. Anal., 2017

2016

Adaptive shrinkage of singular values.

Julie Josse, Sylvain Sardy

Stat. Comput., 2016

Bootstrap-Based Regularization for Low-Rank Matrix Estimation.

Julie Josse, Stefan Wager

J. Mach. Learn. Res., 2016

A principal component method to impute missing values for mixed data.

Vincent Audigier, François Husson, Julie Josse

Adv. Data Anal. Classif., 2016

2015

Regularised PCA to denoise and visualise data.

Marie Verbanck, Julie Josse, François Husson

Stat. Comput., 2015

Stable Autoencoding: A Flexible Framework for Regularized Low-rank Matrix Estimation.

Julie Josse, Stefan Wager

Proceedings of the International Conference on Computational Science, 2015

2012

Selecting the number of components in principal component analysis using cross-validation approximations.

Julie Josse, François Husson

Comput. Stat. Data Anal., 2012

Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis.

Julie Josse, Marie Chavent, Benoit Liquet, François Husson

J. Classif., 2012

2011

Multiple imputation in principal component analysis.

Julie Josse, Jérôme Pagès, François Husson

Adv. Data Anal. Classif., 2011

2008

Testing the significance of the RV coefficient.

Julie Josse, Jérôme Pagès, François Husson

Comput. Stat. Data Anal., 2008

2006

Some theoretical aspects of watermarking detection.

Teddy Furon, Julie Josse, Sandrine Le Squin

Proceedings of the Security, Steganography, and Watermarking of Multimedia Contents VIII, 2006