Biblio
Transformation models for flexible posteriors in variational bayes.
arXiv preprint. 2106.00528. 2106.00528.pdf (1.03 MB)
(2021). Single Shot MC Dropout Approximation.
ICML Workshop on Uncertainty and Robustness in Deep Learning.
(2020). 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)
(2021).
(2020). Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes.
arXiv e-prints. 2010.08376.
(2020). Learning to Cluster.
learning_to_cluster.pdf (1.82 MB)
(2018). DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency.
2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning. AHM2022_DeepDoubt.pdf (238.98 KB)
(2022). Deep probabilistic modelling for energy forecasting.
Poster_Deep probabilistic modelling for energy forecasting TTT.pdf (839.27 KB)
(2023). Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.
(2020).