The COVID-19 global pandemic is a threat not only to the health of millions of individuals,
but also to the stability of infrastructure and economies around the world. The disease will …

Precision medicine requires individualized modeling of disease and drug dynamics, with
machine learning-based computational techniques gaining increasing popularity. The …

Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated
learning (FL) framework in supervised learning. It is inspired by practical applications in …

The world is in the midst of a pandemic. We still know little about the disease COVID-19 or
about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside …

While interest in the application of machine learning to improve healthcare has grown
tremendously in recent years, a number of barriers prevent deployment in medical practice …

Clinical trials are typically run in order to understand the effects of a new treatment on a
given population of patients. However, patients in large populations rarely respond the same …

Recommendation systems have benefited significantly from contextual bandits. Although so
many successful applications and recent advances of contextual bandits to online …

Phase I dose-finding trials are increasingly challenging as the relationship between efficacy
and toxicity of new compounds (or combination of them) becomes more complex. Despite …

Finding an optimal individualized treatment regimen is considered one of the most
challenging precision medicine problems. Various patient characteristics influence the …

We consider the problem of sequentially maximising an unknown function over a set of
actions while ensuring that every sampled point has a function value below a given safety …