The problems studied in the department can be subsumed under the heading of empirical inference, i.e., inference performed on the
basis of empirical data. This includes statistical learning, but also the inference of causal structures from statistical data,
leading to models that provide insight into the underlying mechanisms, and make predictions about the effect of interventions.
Likewise, the type of empirical data can vary, ranging from biological measurements (e.g., in neuroscience) to astronomical
observations. We are conducting theoretical, algorithmic, and experimental studies to try and understand the problem of empirical
The department was started around statistical learning theory and kernel methods. It has since broadened its set of inference
tools to include a stronger component of Bayesian methods, including graphical models with a strong focus on issues of causality.
In terms of the inference tasks being studied, we have moved towards tasks that go beyond the relatively well-studied problem
of supervised learning, such as semisupervised learning or transfer learning. Finally, we have continuously striven to analyze
challenging datasets from biology, astronomy, and other domains, leading to the inclusion of several application areas in our portfolio.
2019 Progess Report Research Overview
Empirical Inference (2010-2015)