Mathematical Modelling and Data Analysis

Artificial intelligence and machine learning tools hold promise to assist or drive high-stakes decisions in areas such as finance, medicine, or autonomous driving. The upcoming AI Act will require that the principles by which such algorithms arrive at their predictions should be transparent. However, the field of XAI is lacking formal problem specifications and theoretical results. In a new study,...

Uncertainty quantification can help to understand the behavior of a trained neural network and, in particular, foster confidence in its predictions. This is especially true for deep regression, where a single-point estimate of a sought function without any information regarding its accuracy can be largely meaningless. We propose a novel framework for benchmarking uncertainty quantification in deep...