Beyond Prediction, Towards Understanding

Research Area A

We will design algorithms that reveal complex structure and causal relationships from data in order to integrate machine learning into the scientific discovery process.

Machine learning needs to improve the methods by which uncertainty can be quantified and handled in large and complex scientific models. This includes techniques to separate uncertainty about data, predictions, parameters and models. We will therefore develop tools to estimate and handle the uncertainty in complex scientific models and algorithms and exploit this information for experimental design. This includes research on the following themes:

  1. Inferring causality
  2. Automatic model criticism
  3. Towards a new level of generalization
  4. Integration of theories and machine learning

Coordinators Research Area A


Matthias Bethge


Bernhard Schölkopf