@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 {694,
	title = {Paths and Ambient Spaces in Neural Loss Landscapes},
	booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
	year = {2025},
	month = {03{\textendash}05 May},
	pages = {10{\textendash}18},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Understanding the structure of neural network loss surfaces, particularly the emergence of low-loss tunnels, is critical for advancing neural network theory and practice. In this paper, we propose a novel approach to directly embed loss tunnels into the loss landscape of neural networks. Exploring the properties of these loss tunnels offers new insights into their length and structure and sheds light on some common misconceptions. We then apply our approach to Bayesian neural networks, where we improve subspace inference by identifying pitfalls and proposing a more natural prior that better guides the sampling procedure.},
	url = {https://proceedings.mlr.press/v258/dold25a.html},
	author = {Dold, Daniel and Kobialka, Julius and Palm, Nicolai and Sommer, Emanuel and R{\"u}gamer, David and D{\"u}rr, Oliver},
	editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}
}
@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 {679,
	title = {Bayesian Calibration of MEMS Accelerometers},
	journal = {IEEE Sensors Journal},
	year = {2023},
	author = {D{\"u}rr, Oliver and Fan, Po-Yu and Yin, Zong-Xian}
}
@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 {682,
	title = {Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays},
	journal = {Global Spine Journal},
	year = {2023},
	pages = {21925682231154543},
	author = {Berlin, Clara and Adomeit, Sonja and Grover, Priyanka and Dreischarf, Marcel and Halm, Henry and D{\"u}rr, Oliver and Obid, Peter}
}
@article {705,
	title = {Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays},
	journal = {Global Spine Journal},
	volume = {14},
	year = {2023},
	pages = {1728{\textendash}1737},
	issn = {2192-5690},
	doi = {10.1177/21925682231154543},
	url = {http://dx.doi.org/10.1177/21925682231154543},
	author = {Berlin, Clara and Adomeit, Sonja and Grover, Priyanka and Dreischarf, Marcel and Halm, Henry and D{\"u}rr, Oliver and Obid, Peter}
}
@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 {683,
	title = {140. Automated measurement technique for coronal parameters using a novel artificial intelligence algorithm: an independent validation study on 100 preoperative AP spine X-rays},
	journal = {The Spine Journal},
	volume = {22},
	year = {2022},
	pages = {S74},
	author = {Berlin, Clara and Adomeit, Sonja and Grover, Priyanka and Dreischarf, Marcel and D{\"u}rr, Oliver and Obid, Peter}
}
@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}
}
@article {685,
	title = {Validation study of an algorithm based on artificial intelligence for automated computation of coronal parameters on preoperative AP X-rays},
	journal = {Brain and Spine},
	volume = {2},
	year = {2022},
	pages = {101156},
	author = {Adomeit, Sonja and Berlin, Clara and Grover, Priyanka and Dreischarf, Marcel and Halm, Henry and D{\"u}rr, Oliver and Obid, Peter}
}
@conference {686,
	title = {Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations},
	booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)},
	year = {2021},
	pages = {398{\textendash}404},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Scharpf, Philipp and Hong, Chi Lap and D{\"u}rr, Oliver}
}
@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}
}
@conference {stadelmann2018capturing,
	title = {Capturing Suprasegmental Features of a Voice with RNNs for Improved Speaker Clustering},
	booktitle = {IAPR Workshop on Artificial Neural Networks in Pattern Recognition},
	year = {2018},
	pages = {333{\textendash}345},
	publisher = {Springer},
	organization = {Springer},
	url = {https://www.springerprofessional.de/capturing-suprasegmental-features-of-a-voice-with-rnns-for-impro/16083698},
	author = {Stadelmann, Thilo and Glinski-Haefeli, Sebastian and Gerber, Patrick and D{\"u}rr, Oliver}
}
@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}
}
@conference {meier2018learning,
	title = {Learning Neural Models for End-to-End Clustering},
	booktitle = {IAPR Workshop on Artificial Neural Networks in Pattern Recognition},
	year = {2018},
	pages = {126{\textendash}138},
	publisher = {Springer},
	organization = {Springer},
	doi = {https://doi.org/10.1007/978-3-319-99978-4_10},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-99978-4_10},
	author = {Meier, Benjamin Bruno and Elezi, Ismail and Amirian, Mohammadreza and D{\"u}rr, Oliver and Stadelmann, Thilo}
}
@conference {lukic2017learning,
	title = {Learning embeddings for speaker clustering based on voice equality},
	booktitle = {Machine Learning for Signal Processing (MLSP), 2017 IEEE 27th International Workshop on},
	year = {2017},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Lukic, Yanick X and Vogt, Carlo and D{\"u}rr, Oliver and Stadelmann, Thilo}
}
@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}
}
@conference {lukic2016speaker,
	title = {Speaker Identification and Clustering using Convolution Neural Networks},
	booktitle = {IEEE International workshop on Machine Learning for Signal Processing},
	year = {2016},
	author = {Lukic, Yanick and Vogt, Carlo and D{\"u}rr, Oliver and Stadelmann, Thilo}
}
@conference {durr2015deep,
	title = {Deep Learning on a Raspberry Pi for Real Time Face Recognition.},
	booktitle = {Eurographics (Posters)},
	year = {2015},
	pages = {11{\textendash}12},
	author = {D{\"u}rr, Oliver and Pauchard, Yves and Browarnik, Diego and Axthelm, Rebekka and Loeser, Martin}
}
@article {franzini2015gene,
	title = {Gene expression signatures predictive of bevacizumab/erlotinib therapeutic benefit in advanced non-squamous non-small cell lung cancer patients (SAKK 19/05 trial)},
	journal = {Clinical Cancer Research},
	year = {2015},
	pages = {clincanres{\textendash}{\textendash}3135},
	publisher = {American Association for Cancer Research},
	author = {Franzini, Anca and Baty, Florent and Macovei, Ina I and D{\"u}rr, Oliver and Droege, Cornelia and Betticher, Daniel and Grigoriu, Bogdan D and Klingbiel, Dirk and Zappa, Francesco and Brutsche, Martin}
}
@conference {durr2014joint_forces,
	title = {JOINT{\_}FORCES: Unite Competing Sentiment Classifiers with Random Forest.},
	booktitle = {SemEval@ COLING},
	year = {2014},
	pages = {366{\textendash}369},
	author = {D{\"u}rr, Oliver and Uzdilli, Fatih and Cieliebak, Mark}
}
@conference {cieliebak2014meta,
	title = {Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools.},
	booktitle = {Language Resources and Evaluation Conference (LREC)},
	year = {2014},
	pages = {3100{\textendash}3104},
	author = {Cieliebak, Mark and D{\"u}rr, Oliver and Uzdilli, Fatih}
}
@article {franzini2014tumor,
	title = {Tumor-associated stromal gene expression signatures predict therapeutic response to erlotinib/bevacizumab in non-small cell lung cancer (NSCLC)},
	journal = {European Respiratory Journal},
	volume = {44},
	number = {Suppl 58},
	year = {2014},
	pages = {P821},
	publisher = {Eur Respiratory Soc},
	author = {Franzini, Anca and D{\"u}rr, Oliver and Baty, Florent and Brutsche, Martin}
}
@conference {cieliebak2013potential,
	title = {Potential and Limitations of Commercial Sentiment Detection Tools.},
	booktitle = {ESSEM@ AI* IA},
	year = {2013},
	pages = {47{\textendash}58},
	publisher = {Citeseer},
	organization = {Citeseer},
	author = {Cieliebak, Mark and D{\"u}rr, Oliver and Uzdilli, Fatih}
}
@article {durr2012using,
	title = {Using Community Structure for Complex Network Layout},
	journal = {arXiv preprint arXiv:1207.6282},
	year = {2012},
	author = {D{\"u}rr, Oliver and Brandenburg, Arnd}
}
@article {durr2007robust,
	title = {Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay},
	journal = {Journal of biomolecular screening},
	volume = {12},
	number = {8},
	year = {2007},
	pages = {1042{\textendash}1049},
	publisher = {Sage Publications Sage CA: Los Angeles, CA},
	author = {D{\"u}rr, Oliver and Duval, Fran{\c c}ois and Nichols, Anthony and Lang, Paul and Brodte, Annette and Heyse, Stephan and Besson, Dominique}
}
@mastersthesis {durr2003theoretical,
	title = {Theoretical Studies of Relaxation and Ionic Transport in Polymers},
	number = {January 2003},
	year = {2003},
	type = {phd},
	isbn = {3898255859},
	url = {http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-10270},
	author = {D{\"u}rr, Oliver}
}
@article {durr2002effective,
	title = {Effective medium theory of conduction in stretched polymer electrolytes},
	journal = {arXiv preprint cond-mat/0202165},
	year = {2002},
	author = {D{\"u}rr, Oliver and Dieterich, Wolfgang and Maas, Philipp and Nitzan, Abraham}
}
@article {durr1998monte,
	title = {Monte-carlo-simulationen zu polymeren ionenleitern},
	year = {1998},
	author = {D{\"u}rr, Oliver}
}
