@conference {692,
	title = {Faster-than-real-time Simulation of Multi-group Pedestrian Flow.},
	booktitle = {Traffic \& Granular Flow 24},
	year = {2025},
	month = {09/2025},
	pages = {04021},
	publisher = {EDP Sciences (334)},
	organization = {EDP Sciences (334)},
	address = {Lyon},
	abstract = {Simulations of pedestrian flow are widely used for design guidelines during the planning of buildings and large events. In this article we discuss how faster-than-real-time simulations of pedestrian flow can contribute to dynamic control strategies of the crowd in order to ensure safety live during an event. For this purpose we present a continuous model for multi-group pedestrian flow. The model consists of the continuity equation for the density of pedestrians and a Helmholtz type equation providing the fastest path to the exit. It is derived by regularizing Hughes{\textquoteright} macroscopic model for pedestrian flow and is solved using the finite element method, which enables simulations at short run-times. The model allows the definition of multiple groups with different fundamental diagrams or destination preferences. We show that this model is able to reproduce qualitatively the expected behavior of crossing pedestrian crowds. Its main advantage is its simulation speed, making it ideal for real-time crowd management at large events.},
	keywords = {FEM Simulation, Kontinuum Model, multi-group, pedestrian flow},
	doi = {https://doi.org/10.1051/epjconf/202533404021},
	author = {M. Schmid and D. Bernhardsgr{\"u}tter and R. Schwarz and D. Bohnet and R. Axthelm}
}
@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 {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}
}
@booklet {703,
	title = {Single-shot Bayesian approximation for neural networks},
	year = {2023},
	url = {https://arxiv.org/abs/2308.12785},
	author = {Kai Brach and Beate Sick and Oliver D{\"u}rr}
}
@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 {642,
	title = {Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations},
	booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)},
	year = {2021},
	month = {07/2021},
	pages = {398-404},
	author = {Philipp Scharpf and Chi Lap Hong and Oliver Duerr}
}
@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}
}
@conference {640,
	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 = {Marcel Arpogaus and Marcus Vo{\ss} and Beate Sick and Mark Nigge-Uricher and Oliver D{\"u}rr}
}
@article {641,
	title = {Transformation models for flexible posteriors in variational bayes},
	journal = {arXiv preprint},
	year = {2021},
	pages = {2106.00528},
	author = {Sefan H{\"o}rtling and Daniel Dold and Oliver D{\"u}rr and Beate Sick}
}
@article {639,
	title = {Deep and interpretable regression models for ordinal outcomes},
	journal = {arXiv preprint},
	year = {2020},
	pages = {2010.08376},
	author = {Lucas Kook and Lisa Herzog and Torsten Hothorn and Oliver D{\"u}rr and Beate Sick}
}
@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}
}
@article {638,
	title = {Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes},
	journal = {arXiv e-prints},
	year = {2020},
	pages = {2010.08376},
	author = {Lucas Kook and Lisa Herzog and Torsten Hothorn and Oliver D{\"u}rr and Beate Sick}
}
@book {621,
	title = {Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability},
	year = {2020},
	publisher = {Manning Publications},
	organization = {Manning Publications},
	isbn = {9781617296079},
	url = {https://www.manning.com/books/probabilistic-deep-learning},
	author = {Oliver D{\"u}rr and Beate Sick and Elvis Murina}
}
@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}
}
@conference {622,
	title = {Single Shot MC Dropout Approximation},
	booktitle = {ICML Workshop on Uncertainty and Robustness in Deep Learning},
	year = {2020},
	author = {Brach, Kai and Beate Sick and Oliver D{\"u}rr}
}
@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 {604,
	title = {Dissecting Multi-Line Handwriting for Multi-Dimensional Connectionist Classification},
	booktitle = {15th IAPR International Conference on Document Analysis and Recognition},
	year = {2019},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Martin Schall and Marc-Peter Schambach and Matthias O. Franz}
}
@conference {617,
	title = {Fast and Reliable Acquisition of Truth Data for Document Analysis using Cyclic Suggest Algorithms},
	booktitle = {ICDAR-OST: The 2nd International Workshop on Open Services and Tools for Document Analysis},
	year = {2019},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Marc-Peter Schambach and Stephan von der N{\"u}ll and Martin Schall}
}
@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}
}
@book {637,
	title = {Learning to Cluster},
	year = {2018},
	author = {Benjamin Bruno Meier and Thilo Stadelmann and Oliver D{\"u}rr}
}
@conference {522,
	title = {LSTM Networks for Edit Distance Calculation with Exchangeable Dictionaries},
	booktitle = {13th IAPR International Workshop on Document Analysis Systems},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Martin Schall and Haiyan P. Buehrig and Marc-Peter Schambach and M O Franz}
}
@conference {521,
	title = {Multi-Dimensional Connectionist Classification: Reading Text in One Step},
	booktitle = {13th IAPR International Workshop on Document Analysis Systems},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Martin Schall and Marc-Peter Schambach and M O Franz}
}
@conference {597,
	title = {Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition},
	booktitle = {Visualization in Data Science (VDS at IEEE VIS) 2018},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Berlin/Germany},
	author = {Martin Schall and Dominik Sacha and Manuel Stein and M O Franz and Daniel A. Keim}
}
@conference {667,
	title = {Detection of variations in holter ECG recordings based on dynamic cluster analysis},
	booktitle = {International Conference on Intelligent Decision Technologies},
	year = {2017},
	author = {Hermann, Matthias and N. Madrid and R. Seepold}
}
@conference {509,
	title = {Radiometric calibration of digital cameras using neural networks},
	booktitle = {Optics and Photonics for Information Processing XI},
	year = {2017},
	month = {08/2017},
	publisher = {SPIE - International Society for Optics and Photonics},
	organization = {SPIE - International Society for Optics and Photonics},
	doi = {10.1117/12.2272559},
	url = {http://dx.doi.org/10.1117/12.2272559},
	author = {Michael Grunwald and Pascal Laube and Martin Schall and Georg Umlauf and M O Franz}
}
@conference {666,
	title = {Analyzing environmental conditions and vital signs to increase healthy living},
	booktitle = {Mobile Networks for Biometric Data Analysis},
	year = {2016},
	author = {R Seepold and C Dermati and A Kostka and L Pfeil and R Lange and Hermann, Matthias and B Martinez}
}
@conference {505,
	title = {Improving gradient-based LSTM training for offline handwriting recognition by careful selection of the optimization method},
	booktitle = {Conference: BW-CAR Symposium on Information and Communication Systems (SInCom)},
	year = {2016},
	month = {12/2016},
	author = {Martin Schall and Marc-Peter Schambach and M O Franz}
}
@conference {491,
	title = {Increasing robustness of handwriting recognition using character n-gram decoding on large lexica},
	booktitle = {12th IAPR International Workshop on Document Analysis Systems},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	keywords = {connectionist temporal classification, lexicon based decoding, long-short-term-memory, n-gram index, offline handwriting recognition, recurrent neural network},
	author = {Martin Schall and Marc-Peter Schambach and M O Franz}
}
@conference {485,
	title = {On-line CAD Reconstruction with Accumulated Means of Local Geometric Properties},
	booktitle = {Curves and Surfaces, 8th International Conference, Paris 2014},
	year = {2015},
	pages = {181-201},
	publisher = {Springer},
	organization = {Springer},
	author = {Klaus Denker and Bernd Hamann and Georg Umlauf},
	editor = {Boissonnat, J-D and Cohen, A and Gibaru, O and Gout, C and Lyche, T and Mazure, M-L and Schumaker, L L}
}
@article {489,
	title = {Pixel-wise Hybrid Image Registration on Wood Decors},
	journal = {BW-CAR| SINCOM},
	year = {2015},
	pages = {24},
	isbn = { 978-3-00-051859-1},
	author = {Michael Grunwald and M{\"u}ller, J and Martin Schall and Pascal Laube and Georg Umlauf and M O Franz}
}
@conference {490,
	title = {Radiometric calibration of digital cameras using Gaussian processes},
	booktitle = {SPIE Optics+ Optoelectronics},
	year = {2015},
	publisher = {International Society for Optics and Photonics},
	organization = {International Society for Optics and Photonics},
	doi = {10.1117/12.2178601},
	author = {Martin Schall and Michael Grunwald and Georg Umlauf and M O Franz}
}
@conference {486,
	title = {{Support Vector Machines for Classification of Geometric Primitives in Point Clouds}},
	booktitle = {Curves and Surfaces, 8th International Conference, Paris 2014},
	year = {2015},
	pages = {80-95},
	publisher = {Springer},
	organization = {Springer},
	author = {Manuel Caputo and Klaus Denker and M O Franz and Pascal Laube and Georg Umlauf},
	editor = {Boissonnat, J-D and Cohen, A and Gibaru, O and Gout, C and Lyche, T and Mazure, M-L and Schumaker, L L}
}
@proceedings {488,
	title = {A Virtual-Reality 3d-Laser-Scan Simulation},
	year = {2015},
	pages = {68},
	author = {Malvin Danhof and Tarek Schneider and Pascal Laube and Georg Umlauf}
}
@conference {183,
	title = {Geometric properties of the adaptice Delaunay tessellation},
	booktitle = {Mathematical Methods of Curves and Surfaces, Tondsberg 2008},
	year = {2010},
	author = {Tom Bobach and Alexandru Constantiniu and Paul Steinmann and Georg Umlauf},
	editor = {D{\ae}hlen, M. and Floater, M.S. and Lyche, T. and Merrien, J.-L. and Morken, K. and Schumaker, L.L.}
}
@article {182,
	title = {The adaptive Delaunay tesselation: A neighborhood covering meshing technique},
	journal = {Computational Mechanics},
	volume = {42},
	year = {2008},
	pages = {655-669},
	author = {Alexandru Constantiniu and Paul Steinmann and Tom Bobach and Gerald Farin and Georg Umlauf}
}
@conference {185,
	title = {Discrete harmonic functions from local coordinates},
	booktitle = {Mathematics of Surfaces XII},
	year = {2007},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	author = {Tom Bobach and Gerald Farin and Dianne Hansford and Georg Umlauf},
	editor = {R.~Martin and M.~Sabin and J.~Winkler}
}
@conference {206,
	title = {Tuning subdivision algorithms using constrained energy minimization},
	booktitle = {Mathematics of Surfaces XII},
	year = {2007},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	author = {Ingo Ginkel and Georg Umlauf},
	editor = {R.~Martin and M.~Sabin and J.~Winkler}
}
@conference {184,
	title = {Controlling a subdivision tuning method},
	booktitle = {Curve and Surface Fitting},
	year = {2006},
	author = {Ingo Ginkel and Georg Umlauf},
	editor = {A.~Cohen and J.-L.~Merrien and L.L.~Schumaker}
}
@conference {219,
	title = {Loop subdivision with curvature control},
	booktitle = {Eurographics Symposium on Geometry Processing},
	year = {2006},
	publisher = {Eurographics},
	organization = {Eurographics},
	author = {Ingo Ginkel and Georg Umlauf},
	editor = {K.~Polthier and A.~Sheffer}
}
@conference {224,
	title = {Triangular G^2 splines},
	booktitle = {Curve and Surface Design},
	year = {1999},
	publisher = {Vanderbilt University Press},
	organization = {Vanderbilt University Press},
	author = {Hartmut Prautzsch and Georg Umlauf},
	editor = {P.-L.~Laurent and P.~Sablonniere and L.L.~Schumaker}
}
