On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Bridges are often located in harsh environments and are thus extremely susceptible to
damage. If the initial damage is not detected in time, it can develop further causing safety …

Abstract Model-based reinforcement learning (RL) algorithms can attain excellent sample
efficiency, but often lag behind the best model-free algorithms in terms of asymptotic …

Dealing with uncertainty is essential for efficient reinforcement learning. There is a growing
literature on uncertainty estimation for deep learning from fixed datasets, but many of the …

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target
data set by only using a pre-trained source model. However, the absence of the source data …

Intelligence relies on an agent's knowledge of what it does not know. This capability can be
assessed based on the quality of joint predictions of labels across multiple inputs. In …

The interdependence between nodes in graphs is key to improve class prediction on nodes,
utilized in approaches like Label Probagation (LP) or in Graph Neural Networks (GNNs) …

Modern machine learning (ML) techniques, in conjunction with simulation-based methods,
present remarkable potential for various scientific and engineering applications. Within the …

Even though probabilistic treatments of neural networks have a long history, they have not
found widespread use in practice. Sampling approaches are often too slow already for …

Bayesian reward learning from demonstrations enables rigorous safety and uncertainty
analysis when performing imitation learning. However, Bayesian reward learning methods …