Deep probabilistic direction prediction in 3D with applications to directional dark matter
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As machine learning systems become increasingly integrated into real-world applications,
accurately representing uncertainty is crucial for enhancing their safety, robustness, and …

Neural networks that can produce accurate, input-conditional uncertainty representations
are critical for real-world applications. Recent progress on heteroscedastic continuous …

Regression models are of fundamental importance in explicitly explaining the response
variable in terms of covariates. However, point predictions of these models limit them from …

This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric
uncertainty, which refers to the inherent variability in the input-output relationships of a …

Deep neural networks and large-scale datasets have revolutionized the field of machine
learning. However, these large networks are susceptible to overfitting to label noise …

The availability of large amounts of experimental and computational data together with
powerful computers enabled use of machine learning to predict phenomena in physical …

This thesis aims to develop and present methods to model the uncertainty in transportation
systems and machine learning models. This is motivated by the complex and dynamic …