Digital health data are multimodal and high-dimensional. A patient's health state can be
characterized by a multitude of signals including medical imaging, clinical variables …

Computational social science is more than just large repositories of digital data and the
computational methods needed to construct and analyse them. It also represents a …

Creating machines that behave and work in a way similar to humans is the objective of
artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving …

The goal of domain generalization algorithms is to predict well on distributions different from
those seen during training. While a myriad of domain generalization algorithms exist …

Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Data deletion algorithms aim to remove the influence of deleted data points from trained
models at a cheaper computational cost than fully retraining those models. However, for …

Causal reasoning can shed new light on the major challenges in machine learning for
medical imaging: scarcity of high-quality annotated data and mismatch between the …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

The global SARS-CoV-2 outbreak and subsequent lockdown had a significant impact on
people's daily lives, with strong implications for stress levels due to the threat of contagion …

While early AutoML frameworks focused on optimizing traditional ML pipelines and their
hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this …