Biblio
Deep and interpretable regression models for ordinal outcomes.
arXiv preprint. 2010.08376.
(2020). Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes.
arXiv e-prints. 2010.08376.
(2020).
(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). Single Shot MC Dropout Approximation.
ICML Workshop on Uncertainty and Robustness in Deep Learning.
(2020). Transformation models for flexible posteriors in variational bayes.
arXiv preprint. 2106.00528. 2106.00528.pdf (1.03 MB)
(2021).