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.

• BibTeX

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.

• BibTeX

2106.00528.pdf (1.03 MB)

Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Deep and interpretable regression models for ordinal outcomes. arXiv preprint. 2010.08376.

• BibTeX

Kook, L., Herzog L., Hothorn T., Dürr O., & Sick B. (2020).  Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes. arXiv e-prints. 2010.08376.

• BibTeX

Dürr, O., Sick B., & Murina E. (2020).  Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability.

• BibTeX

Brach, K., Sick B., & Dürr O. (2020).  Single Shot MC Dropout Approximation. ICML Workshop on Uncertainty and Robustness in Deep Learning.

• BibTeX