Marcel Arpogaus — accepted AI-conference papers

2025

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals

Marcel Arpogaus, Thomas Kneib, Thomas Nagler, David Rügamer

UAI 2025

Density regression models allow a comprehensive understanding of data by modeling the complete conditional probability distribution. While flexible estimation approaches such as normalizing flows (NFs) work particularly well in multiple dimensions, interpreting the input-output relationship of such

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2024

How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression

Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz +5

UAI 2024poster

Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms. However, neural representations of distributional regression models, such as the Cox model, have received little atte…

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