Reinforcement learning (RL) can assist in medical decision making using patient data
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …

Many reinforcement learning (RL) applications have combinatorial action spaces, where
each action is a composition of sub-actions. A standard RL approach ignores this inherent …

With the recent advances in machine learning for quantum chemistry, it is now possible to
predict the chemical properties of compounds and to generate novel molecules. Existing …

The ability to answer causal questions is crucial in many domains, as causal inference
allows one to understand the impact of interventions. In many applications, only a single …

Estimating an individual's potential response to interventions from observational data is of
high practical relevance for many domains, such as healthcare, public policy or economics …

Abstract Domain shift, encountered when using a trained model for a new patient
population, creates significant challenges for sequential decision making in healthcare since …

Identifying meaningful and independent factors of variation in a dataset is a challenging
learning task frequently addressed by means of deep latent variable models. This task can …

Symmetry transformations induce invariances and are a crucial building block of modern
machine learning algorithms. In many complex domains, such as the chemical space …

In healthcare, clinicians constantly make decisions about when and how to treat each
patient. These decisions are based on medical training and clinical experience, but they …

The beginning of the 21st century also saw a new beginning for an entirely new generation
of technology, software, and computers. The amount of time, effort, money, and research …