Uncertainty Quantification of Machine Learning Models

Machine learning models have become significantly more popular in recent years and are increasingly being used in areas where reliability is crucial. Think, for example, of self-driving cars or analyzing CT scans. To have confidence in a model, it is necessary to quantify its uncertainty. Since machine learning models differ from classical models in crucial ways – they typically have more parameters than data points and are slightly different each time they are created – classical techniques for quantifying uncertainty cannot be directly applied. This work provides new contributions to address this problem.