Max Planck Institute for Intelligent Systems
72076 Tübingen, Germany
Phone: +49 (0)7071 601 551
The problems studied in the Schölkopf department at the MPI 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 inference.
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 recent 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 semi-supervised 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. No matter whether the applications are done in the department or in collaboration with external partners, considering a whole range of applications helps us study principles and methods of inference, rather than inference applied to one specific problem domain.