5 citations · 9 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
Domagoj Ćevid, Loris Michel, Jeffrey Näf +2
Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm. Part of its appeal and reason for its versatility is its (implicit) construct…