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ORCIDPhilipp F. M. Baumann
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2020 – today
- 2024
[i5]Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer:
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression. CoRR abs/2405.05429 (2024)- 2023
- [j5]Philipp F. M. Baumann, Enzo Rossi, Alexander Volkmann
:
What drives inflation and how? Evidence from additive mixed models selected by cAIC. Frontiers Appl. Math. Stat. 9 (2023) - [j4]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:
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. J. Stat. Softw. 105(2) (2023) - [j3]Theresa Ullmann, Stefanie Peschel, Philipp F. M. Baumann, Christian L. Müller, Anne-Laure Boulesteix:
Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering. PLoS Comput. Biol. 19(1) (2023) - [j2]David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn:
Probabilistic time series forecasts with autoregressive transformation models. Stat. Comput. 33(2): 37 (2023) - 2022
- [j1]David Rügamer, Philipp F. M. Baumann, Sonja Greven:
Selective inference for additive and linear mixed models. Comput. Stat. Data Anal. 167: 107350 (2022) - [i4]Lucas Kook, Andrea Götschi, Philipp F. M. Baumann, Torsten Hothorn, Beate Sick:
Deep interpretable ensembles. CoRR abs/2205.12729 (2022) - 2021
- [c1]Philipp F. M. Baumann, Torsten Hothorn, David Rügamer:
Deep Conditional Transformation Models. ECML/PKDD (3) 2021: 3-18 - [i3]Max Horn, Kumar Shridhar, Elrich Groenewald, Philipp F. M. Baumann:
Translational Equivariance in Kernelizable Attention. CoRR abs/2102.07680 (2021) - [i2]David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn:
Transforming Autoregression: Interpretable and Expressive Time Series Forecast. CoRR abs/2110.08248 (2021) - 2020
- [i1]Philipp F. M. Baumann, Torsten Hothorn, David Rügamer:
Deep Conditional Transformation Models. CoRR abs/2010.07860 (2020)
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