Biblio
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(2025). Faster-than-real-time Simulation of Multi-group Pedestrian Flow..
Traffic & Granular Flow 24. 04021.
(2025). Faster-than-real-time Simulation of Multi-group Pedestrian Flow..
Traffic & Granular Flow 24. 04021.
(2025). Interpretable Neural Causal Models with TRAM-DAGs.
(Huang, B., & Drton M., Ed.).Proceedings of the Fourth Conference on Causal Learning and Reasoning. 606–630.
(2025). Paths and Ambient Spaces in Neural Loss Landscapes.
(Li, Y., Mandt S., Agrawal S., & Khan E., Ed.).Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. 10–18.
(2024). Bayesian Semi-structured Subspace Inference.
(Dasgupta, S., Mandt S., & Li Y., Ed.).Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. 1819–1827.
(2024). Bernstein flows for flexible posteriors in variational Bayes.
AStA Advances in Statistical Analysis. 108, 375–394.
(2024). Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo.
Journal of Statistical Software. 111,
(2023). Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(2023). Short-term density forecasting of low-voltage load using Bernstein-polynomial normalizing flows.
IEEE Transactions on Smart Grid.
(2022). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2021). Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations.
2021 International Conference on Data Mining Workshops (ICDMW). 398-404.
(2021). Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations.
2021 International Conference on Data Mining Workshops (ICDMW). 398–404.
(2021). Deep transformation models: Tackling complex regression problems with neural network based transformation models.
Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021.
(2021). Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows.
ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual.
(2021). Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows.
ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual. 
Arpogaus2021_Probabilistic_Forecasting.pdf (427.35 KB)
Arpogaus2021_Probabilistic_Forecasting.pdf (427.35 KB)
(2021). Transformation models for flexible posteriors in variational bayes.
arXiv preprint. 2106.00528. 2106.00528.pdf (1.03 MB)
2106.00528.pdf (1.03 MB)
(2020). Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.
(2020). Integrating uncertainty in deep neural networks for MRI based stroke analysis.
Medical Image Analysis. 65, 101790.
(2020). Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes.
arXiv e-prints. 2010.08376.
(2020). Single Shot MC Dropout Approximation.
ICML Workshop on Uncertainty and Robustness in Deep Learning.
(2019). Beyond ImageNet: Deep Learning in Industrial Practice.
Applied Data Science. 205-232.
(2019). Beyond ImageNet: Deep Learning in Industrial Practice.
Applied Data Science. 205-232.