3 papers
stat.ML2026
When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
Christophe Muller, Erwan Scornet, Julie Josse
Predicting with missing inputs challenges even parametric models, as parameter estimation alone is insufficient for prediction on incomplete data. While several works study predict…
stat.CO2025
Do we Need Dozens of Methods for Real World Missing Value Imputation?
Krystyna Grzesiak, Christophe Muller, Julie Josse +1
Missing values pose a persistent challenge in modern data science. Consequently, there is an ever-growing number of publications introducing new imputation methods in various field…
stat.ME2024
Predictive Uncertainty Quantification with Missing Covariates
Margaux Zaffran, Julie Josse, Yaniv Romano +1
Predictive uncertainty quantification is crucial in decision-making problems. We investigate how to adequately quantify predictive uncertainty with missing covariates. A bottleneck…