Philipp F. M. Baumann

According to our database, Philipp F. M. Baumann authored at least 10 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer

CoRR, 2024

2023

Probabilistic time series forecasts with autoregressive transformation models.

David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn

Stat. Comput., April, 2023

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.

Theresa Ullmann, Stefanie Peschel, Philipp F. M. Baumann, Christian L. Müller, Anne-Laure Boulesteix

PLoS Comput. Biol., January, 2023

deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.

David Rügamer, Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp F. M. Baumann, Lucas Kook, Nadja Klein, Christian L. Müller

J. Stat. Softw., 2023

What drives inflation and how? Evidence from additive mixed models selected by cAIC.

Philipp F. M. Baumann, Enzo Rossi, Alexander Volkmann

Frontiers Appl. Math. Stat., 2023

2022

Selective inference for additive and linear mixed models.

David Rügamer, Philipp F. M. Baumann, Sonja Greven

Comput. Stat. Data Anal., 2022

Deep interpretable ensembles.

Lucas Kook, Andrea Götschi, Philipp F. M. Baumann, Torsten Hothorn, Beate Sick

CoRR, 2022

2021

Transforming Autoregression: Interpretable and Expressive Time Series Forecast.

David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn

CoRR, 2021

Translational Equivariance in Kernelizable Attention.

Max Horn, Kumar Shridhar, Elrich Groenewald, Philipp F. M. Baumann

CoRR, 2021

Deep Conditional Transformation Models.

Philipp F. M. Baumann, Torsten Hothorn, David Rügamer

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021