Biblio

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2021
Arpogaus, M., Voß M., Sick B., Nigge-Uricher M., & Dürr O. (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.
Arpogaus, M., Voß M., Sick B., Nigge-Uricher M., & Dürr O. (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. PDF icon Arpogaus2021_Probabilistic_Forecasting.pdf (427.35 KB)
Hörtling, S., Dold D., Dürr O., & Sick B. (2021).  Transformation models for flexible posteriors in variational bayes. arXiv preprint. 2106.00528.PDF icon 2106.00528.pdf (1.03 MB)
2022
Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2022).  Deep and interpretable regression models for ordinal outcomes. Pattern Recognition. 122, 108263.
2023
Herzog, L., Kook L., Götschi A., Petermann K., Hänsel M., Hamann J., et al. (2023).  Deep transformation models for functional outcome prediction after acute ischemic stroke. Biometrical Journal. 65, 2100379.
Arpogaus, M., Voss M., Sick B., Nigge-Uricher M., & Dürr O. (2023).  Short-term density forecasting of low-voltage load using Bernstein-polynomial normalizing flows. IEEE Transactions on Smart Grid.
Brach, K., Sick B., & Dürr O. (2023).  Single-shot Bayesian approximation for neural networks.
2024
Dold, D., Ruegamer D., Sick B., & Dürr O. (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.
Dürr, O., Hörtling S., Dold D., Kovylov I., & Sick B. (2024).  Bernstein flows for flexible posteriors in variational Bayes. AStA Advances in Statistical Analysis. 108, 375–394.
Kook, L., Baumann P. F. M., Dürr O., Sick B., & Rügamer D. (2024).  Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo. Journal of Statistical Software. 111,
2025
Schmid, M., Bernhardsgrütter D., Schwarz R., Bohnet D., & Axthelm R. (2025).  Faster-than-real-time Simulation of Multi-group Pedestrian Flow.. Traffic & Granular Flow 24. 04021.
Schmid, M., Bernhardsgrütter D., Schwarz R., Bohnet D., & Axthelm R. (2025).  Faster-than-real-time Simulation of Multi-group Pedestrian Flow.. Traffic & Granular Flow 24. 04021.
Sick, B., & Dürr O. (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.
Dold, D., Kobialka J., Palm N., Sommer E., Rügamer D., & Dürr O. (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.

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