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stat.ML2026
Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
Gitte Kremling, Jeffrey Näf, Johannes Lederer
The prevalence of missing values in data science poses a substantial risk to any further analyses. Despite a wealth of research, principled nonparametric methods to deal with gener…
stat.ML2026
CLVAE: A Variational Autoencoder for Long-Term Customer Revenue Forecasting
Jeffrey Näf, Riana Valera Mbelson, Markus Meierer
Predicting customers' long-term revenue from sparse and irregular transaction data is central to marketing resource allocation in non-contractual settings, yet existing approaches…
stat.ML2023
MMD-based Variable Importance for Distributional Random Forest
Clément Bénard, Jeffrey Näf, Julie Josse
Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables. In th…