Thomas Kneib — 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

Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean

Anton Frederik Thielmann, René-Marcel Kruse, Thomas Kneib, Benjamin Säfken

AISTATS 2024poster

Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making them difficult to interpret or understand. This la…

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