@conference {707,
	title = {Interpretable Neural Causal Models with TRAM-DAGs},
	booktitle = {Proceedings of the Fourth Conference on Causal Learning and Reasoning},
	year = {2025},
	month = {07{\textendash}09 May},
	pages = {606{\textendash}630},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {The ultimate goal of most scientific studies is to understand the underlying causal mechanism between the involved variables. Structural causal models (SCMs) are widely used to represent such causal mechanisms. Given an SCM, causal queries on all three levels of Pearl{\textquoteright}s causal hierarchy can be answered: $\mathcalL_1$ observational, $\mathcalL_2$ interventional, and $\mathcalL_3$ counterfactual. An essential aspect of modeling the SCM is to model the dependency of each variable on its causal parents. Traditionally this is done by parametric statistical models, such as linear or logistic regression models. This allows to handle all kinds of data types and fit interpretable models but bears the risk of introducing a bias due to the assumed rigid functional form. More recently neural causal models came up using neural networks (NNs) to model the causal relationships, allowing the estimation of nearly any underlying functional form without bias. However, current neural causal models are generally restricted to continuous variables and do not yield an interpretable form of the causal relationships. Transformation models range from simple statistical regressions to complex networks and can handle continuous, ordinal, and binary data. Here, we propose to use TRAMs to model the functional relationships in SCMs allowing us to bridge the gap between interpretability and flexibility in causal modeling. We call this method TRAM-DAG and assume currently that the underlying directed acyclic graph (DAG) is known. For the fully observed case, we benchmark TRAM-DAGs against state-of-the-art statistical and NN-based causal models. We show that TRAM-DAGs are interpretable but also achieve equal or superior performance in queries ranging from $\mathcalL_1$ to $\mathcalL_3$ in the causal hierarchy. For the continuous case, TRAM-DAGs allow for counterfactual queries for three common causal structures, including unobserved confounding.},
	url = {https://proceedings.mlr.press/v275/sick25a.html},
	author = {Sick, Beate and D{\"u}rr, Oliver},
	editor = {Huang, Biwei and Drton, Mathias}
}
@conference {693,
	title = {Bayesian Semi-structured Subspace Inference},
	booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
	year = {2024},
	month = {02{\textendash}04 May},
	pages = {1819{\textendash}1827},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. The structured model part is inspired by statistical models and can be used to infer the input-output relationship for features of particular importance. The complex unstructured part defines an arbitrary deep neural network and thereby provides enough flexibility to achieve competitive prediction performance. While these models can also account for aleatoric uncertainty, there is still a lack of work on accounting for epistemic uncertainty. In this paper, we address this problem by presenting a Bayesian approximation for semi-structured regression models using subspace inference. To this end, we extend subspace inference for joint posterior sampling from a full parameter space for structured effects and a subspace for unstructured effects. Apart from this hybrid sampling scheme, our method allows for tunable complexity of the subspace and can capture multiple minima in the loss landscape. Numerical experiments validate our approach{\textquoteright}s efficacy in recovering structured effect parameter posteriors in semi-structured models and approaching the full-space posterior distribution of MCMC for increasing subspace dimension. Further, our approach exhibits competitive predictive performance across simulated and real-world datasets.},
	url = {https://proceedings.mlr.press/v238/dold24a.html},
	author = {Dold, Daniel and Ruegamer, David and Sick, Beate and D{\"u}rr, Oliver},
	editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}
}
@article {704,
	title = {Bernstein flows for flexible posteriors in variational Bayes},
	journal = {AStA Advances in Statistical Analysis},
	volume = {108},
	year = {2024},
	pages = {375{\textendash}394},
	issn = {1863-818X},
	doi = {10.1007/s10182-024-00497-z},
	url = {http://dx.doi.org/10.1007/s10182-024-00497-z},
	author = {D{\"u}rr, Oliver and H{\"o}rtling, Stefan and Dold, Danil and Kovylov, Ivonne and Sick, Beate}
}
@article {706,
	title = {Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo},
	journal = {Journal of Statistical Software},
	volume = {111},
	year = {2024},
	issn = {1548-7660},
	doi = {10.18637/jss.v111.i10},
	url = {http://dx.doi.org/10.18637/jss.v111.i10},
	author = {Kook, Lucas and Baumann, Philipp F. M. and D{\"u}rr, Oliver and Sick, Beate and R{\"u}gamer, David}
}
@article {680,
	title = {Deep transformation models for functional outcome prediction after acute ischemic stroke},
	journal = {Biometrical Journal},
	volume = {65},
	year = {2023},
	pages = {2100379},
	author = {Herzog, Lisa and Kook, Lucas and G{\"o}tschi, Andrea and Petermann, Katrin and H{\"a}nsel, Martin and Hamann, Janne and D{\"u}rr, Oliver and Wegener, Susanne and Sick, Beate}
}
@article {681,
	title = {Short-term density forecasting of low-voltage load using Bernstein-polynomial normalizing flows},
	journal = {IEEE Transactions on Smart Grid},
	year = {2023},
	author = {Arpogaus, Marcel and Voss, Marcus and Sick, Beate and Nigge-Uricher, Mark and D{\"u}rr, Oliver}
}
@article {684,
	title = {Deep and interpretable regression models for ordinal outcomes},
	journal = {Pattern Recognition},
	volume = {122},
	year = {2022},
	pages = {108263},
	author = {Kook, Lucas and Herzog, Lisa and Hothorn, Torsten and D{\"u}rr, Oliver and Sick, Beate}
}
@conference {624,
	title = {Deep transformation models: Tackling complex regression problems with neural network based transformation models},
	booktitle = {Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021},
	year = {2021},
	author = {Sick, Beate and Hothorn, Torsten and D{\"u}rr, Oliver}
}
@conference {687,
	title = {Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows},
	booktitle = {ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual},
	year = {2021},
	author = {Arpogaus, Marcel and Vo{\ss}, Marcus and Sick, Beate and Nigge-Uricher, Mark and D{\"u}rr, Oliver}
}
@article {623,
	title = {Integrating uncertainty in deep neural networks for MRI based stroke analysis},
	journal = {Medical Image Analysis},
	volume = {65},
	year = {2020},
	pages = {101790},
	author = {Herzog, Lisa and Murina, Elvis and D{\"u}rr, Oliver and Wegener, Susanne and Sick, Beate}
}
@book {688,
	title = {Probabilistic deep learning: With python, keras and tensorflow probability},
	year = {2020},
	publisher = {Manning Publications},
	organization = {Manning Publications},
	author = {D{\"u}rr, Oliver and Sick, Beate and Murina, Elvis}
}
@inbook {614,
	title = {Beyond ImageNet: Deep Learning in Industrial Practice},
	booktitle = {Applied Data Science},
	year = {2019},
	pages = {205-232},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-030-11820-4},
	issn = {978-3-030-11821-1},
	doi = {https://doi.org/10.1007/978-3-030-11821-1_12},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-030-11821-1_12},
	author = {Stadelmann, Thilo and Tolkachev, Vasily and Sick, Beate and D{\"u}rr, Oliver}
}
@article {613,
	title = {Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks},
	year = {2019},
	url = {https://www.journals.elsevier.com/computers-and-electrical-engineering},
	author = {Rohrbach, Janick and Rainhard, Tobias and Sick, Beate and D{\"u}rr, Oliver}
}
@conference {616,
	title = {Automatic classification of non-small cell lung cancer histologic sub-types by deep learning},
	booktitle = {VIRCHOWS ARCHIV},
	year = {2018},
	month = {09/18},
	pages = {108-108},
	publisher = {Springer},
	organization = {Springer},
	author = {Casanova, R and Murina, Elvis and Haberecker, M and Honcharova-Biletska, H and Vrugt, B and D{\"u}rr, Oliver and Sick, Beate and Soltermann, A}
}
@article {593,
	title = {Developing deep learning applications for life science and pharma industry},
	journal = {Drug research},
	volume = {68},
	year = {2018},
	pages = {305{\textendash}310},
	doi = {https://doi.org/10.1055/s-0043-124761},
	url = {https://doi.org/10.1055/s-0043-124761},
	author = {Siegismund, Daniel and Tolkachev, Vasily and Heyse, Stephan and Sick, Beate and D{\"u}rr, Oliver and Steigele, Stephan}
}
@article {durr2018know,
	title = {Know When You Don{\textquoteright}t Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes},
	journal = {Assay and drug development technologies},
	volume = {16},
	number = {6},
	year = {2018},
	pages = {343{\textendash}349},
	publisher = {Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA},
	url = {https://www.liebertpub.com/doi/10.1089/adt.2018.859},
	author = {D{\"u}rr, Oliver and Murina, Elvis and Siegismund, Daniel and Tolkachev, Vasily and Steigele, Stephan and Sick, Beate}
}
@article {durr2016single,
	title = {Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks},
	journal = {Journal of biomolecular screening},
	volume = {21},
	number = {9},
	year = {2016},
	pages = {998{\textendash}1003},
	publisher = {SAGE Publications},
	doi = {https://doi.org/10.1177/1087057116631284},
	url = {https://doi.org/10.1177/1087057116631284},
	author = {D{\"u}rr, Oliver and Sick, Beate}
}
