Some theoretical aspects of watermarking detection. - Teddy Furon; Julie Josse; Sandrine Le Squin (Security, Steganography, and Watermarking of Multimedia Contents, 2006)

Testing the significance of the RV coefficient. - Julie Josse; Jérôme Pagès; François Husson (Comput. Stat. Data Anal., 2008)

Multiple imputation in principal component analysis. - Julie Josse; Jérôme Pagès; François Husson (Adv. Data Anal. Classif., 2011)

Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis. - Julie Josse; Marie Chavent; Benoit Liquet; François Husson (J. Classif., 2012)

Selecting the number of components in principal component analysis using cross-validation approximations. - Julie Josse; François Husson (Comput. Stat. Data Anal., 2012)

Stable Autoencoding: A Flexible Framework for Regularized Low-rank Matrix Estimation. - Julie Josse; Stefan Wager (ICCS, 2015)

Regularised PCA to denoise and visualise data. - Marie Verbanck; Julie Josse; François Husson (Stat. Comput., 2015)

A principal component method to impute missing values for mixed data. - Vincent Audigier; François Husson; Julie Josse (Adv. Data Anal. Classif., 2016)

Bootstrap-Based Regularization for Low-Rank Matrix Estimation. - Julie Josse; Stefan Wager (J. Mach. Learn. Res., 2016)

Adaptive shrinkage of singular values. - Julie Josse; Sylvain Sardy (Stat. Comput., 2016)

Multiple correspondence analysis and the multilogit bilinear model. - William Fithian; Julie Josse (J. Multivar. Anal., 2017)

MIMCA: multiple imputation for categorical variables with multiple correspondence analysis. - Vincent Audigier; François Husson; Julie Josse (Stat. Comput., 2017)

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 (ESANN, 2018)

Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames. - Geneviève Robin; Hoi-To Wai; Julie Josse; Olga Klopp; Eric Moulines (NeurIPS, 2018)

Imputation and low-rank estimation with Missing Non At Random data. - Aude Sportisse; Claire Boyer; Julie Josse (CoRR, 2018)

On the consistency of supervised learning with missing values. - Julie Josse; Nicolas Prost; Erwan Scornet; Gaël Varoquaux (CoRR, 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)

Low-rank model with covariates for count data with missing values. - Geneviève Robin; Julie Josse; Eric Moulines; Sylvain Sardy (J. Multivar. Anal., 2019)

Linear predictor on linearly-generated data with missing values: non consistency and solutions. - Marine Le Morvan; Nicolas Prost; Julie Josse; Erwan Scornet; Gaël Varoquaux (AISTATS, 2020)

Multivariate Analysis is Sufficient for Lesion-Behaviour Mapping. - Lucas Martin; Julie Josse; Bertrand Thirion (BrainLes@MICCAI (1), 2020)

Missing Data Imputation using Optimal Transport. - Boris Muzellec; Julie Josse; Claire Boyer; Marco Cuturi (ICML, 2020)

NeuMiss networks: differentiable programming for supervised learning with missing values. - Marine Le Morvan; Julie Josse; Thomas Moreau 001; Erwan Scornet; Gaël Varoquaux (NeurIPS, 2020)

Debiasing Averaged Stochastic Gradient Descent to handle missing values. - Aude Sportisse; Claire Boyer; Aymeric Dieuleveut; Julie Josse (NeurIPS, 2020)

Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data. - Aude Sportisse; Claire Boyer; Julie Josse (NeurIPS, 2020)

MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models. - Imke Mayer; Julie Josse; Félix Raimundo; Jean-Philippe Vert (CoRR, 2020)

Neumann networks: differential programming for supervised learning with missing values. - Marine Le Morvan; Julie Josse; Thomas Moreau 001; Erwan Scornet; Gaël Varoquaux (CoRR, 2020)

Logistic regression with missing covariates - Parameter estimation, model selection and prediction within a joint-modeling framework. - Wei Jiang 027; Julie Josse; Marc Lavielle; TraumaBase Group (Comput. Stat. Data Anal., 2020)

Imputation and low-rank estimation with Missing Not At Random data. - Aude Sportisse; Claire Boyer; Julie Josse (Stat. Comput., 2020)

What's a good imputation to predict with missing values? - Marine Le Morvan; Julie Josse; Erwan Scornet; Gaël Varoquaux (NeurIPS, 2021)

Adaptive Conformal Predictions for Time Series. - Margaux Zaffran; Olivier Féron; Yannig Goude; Julie Josse; Aymeric Dieuleveut (ICML, 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 Bayesian SLOPE: Model Selection With Incomplete Data. - Wei Jiang 027; Malgorzata Bogdan; Julie Josse; Szymon Majewski; Blazej Miasojedow; Veronika Rocková (J. Comput. Graph. Stat., 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)

Conformal Prediction with Missing Values. - Margaux Zaffran; Aymeric Dieuleveut; Julie Josse; Yaniv Romano (ICML, 2023)

MMD-based Variable Importance for Distributional Random Forest. - Clément Bénard; Jeffrey Näf; Julie Josse (AISTATS, 2024)

Positivity-free Policy Learning with Observational Data. - Pan Zhao; Antoine Chambaz; Julie Josse; Shu Yang (AISTATS, 2024)

Federated Causal Inference: Multi-Centric ATE Estimation beyond Meta-Analysis. - Rémi Khellaf; Aurélien Bellet; Julie Josse (CoRR, 2024)

Expert Study on Interpretable Machine Learning Models with Missing Data. - Lena Stempfle; Arthur James; Julie Josse; Tobias Gauss; Fredrik D. Johansson (CoRR, 2024)

Model-based clustering with missing not at random data. - Aude Sportisse; Matthieu Marbac; Fabien Laporte; Gilles Celeux; Claire Boyer; Julie Josse; Christophe Biernacki (Stat. Comput., 2024)

Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis. - Rémi Khellaf; Aurélien Bellet; Julie Josse (AISTATS, 2025)

Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments. - Houssam Zenati; Judith Abécassis; Julie Josse; Bertrand Thirion (AISTATS, 2025)

Probabilistic prediction of arrivals and hospitalizations in emergency departments in Île-de-France. - Herbert Susmann; Antoine Chambaz; Julie Josse; Philippe Aegerter; Mathias Wargon; Emmanuel Bacry (Int. J. Medical Informatics, 2025)

Optimal Transport with Heterogeneously Missing Data. - Linus Bleistein; Aurélien Bellet; Julie Josse (CoRR, 2025)

Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation. - Rémi Khellaf; Aurélien Bellet; Julie Josse (CoRR, 2025)

Model Agnostic Differentially Private Causal Inference. - Christian Janos Lebeda; Mathieu Even; Aurélien Bellet; Julie Josse (CoRR, 2025)

When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values. - Christophe Muller; Erwan Scornet; Julie Josse (CoRR, 2025)