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 …

Reliable uncertainty from deterministic single-forward pass models is sought after because
conventional methods of uncertainty quantification are computationally expensive. We take …

Being able to quantify uncertainty when comparing a theoretical or computational model to
observations is critical to conducting a sound scientific investigation. With the rise of data …

There has been significant research done on developing methods for improving robustness
to distributional shift and uncertainty estimation. In contrast, only limited work has examined …

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 …

We show that a single softmax neural net with minimal changes can beat the uncertainty
predictions of Deep Ensembles and other more complex single-forward-pass uncertainty …

Anomaly detection can be conceived either through generative modelling of regular training
data or by discriminating with respect to negative training data. These two approaches …

Deep kernel learning and related techniques promise to combine the representational
power of neural networks with the reliable uncertainty estimates of Gaussian processes. One …

Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes
with more evidence. While existing work focuses on using the variance of the Bayesian …

Accurate uncertainty quantification is a major challenge in deep learning, as neural
networks can make overconfident errors and assign high confidence predictions to out-of …