Decision-making based on machine learning systems, especially when this decision-making
can affect human lives, is a subject of maximum interest in the Machine Learning community …

Drug-induced liver injury (DILI) is a common reason for the withdrawal of a drug from the
market. Early assessment of DILI risk is an essential part of drug development, but it is …

Deep learning is becoming an increasingly important way to produce accurate hydrological
predictions across a wide range of spatial and temporal scales. Uncertainty estimations are …

The Gaussianity assumption has been consistently criticized as a main limitation of the
Variational Autoencoder (VAE) despite its efficiency in computational modeling. In this …

Quantile Regression (QR) provides a way to approximate a single conditional quantile. To
have a more informative description of the conditional distribution, QR can be merged with …

Light detection and ranging (LiDAR) limitations in adverse weather (eg, rain, fog, and snow)
prevent adopting high-level autonomous vehicles in all weather conditions. Furthermore …

Deep learning models achieve state-of-the art results in predicting blood glucose
trajectories, with a wide range of architectures being proposed. However, the adaptation of …

Safety standards in domains like automotive and avionics seek for deterministic execution
(lack of jittery behavior) as a stepping stone to build a certification argument on the correct …

Despite the major progress of deep models as learning machines, uncertainty estimation
remains a major challenge. Existing solutions rely on modified loss functions or architectural …

Abstract Conditional Density Estimation (CDE) has wide-reaching applicability to various
real-world problems, such as spatial density estimation and environmental modelling. CDE …