Biblio
Transformation models for flexible posteriors in variational bayes.
arXiv preprint. 2106.00528.
2106.00528.pdf (1.03 MB)
(2021). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
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). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Deep probabilistic modelling for energy forecasting.
Poster_Deep probabilistic modelling for energy forecasting TTT.pdf (839.27 KB)
(2023). ![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.
(2020).