The goal of statistical learning theory is to provide solid theoretical analysis of the behavior of machine learning algorithms. Under the assumption that the data has been sampled from some underlying, but unknown ground truth, we want to assess whether the results achieved by machine learning algorithms are trustworthy, whether the algorithms are well-behaved or erratic, or what is their complexity in terms of data required or computation time needed.

Some branches of statistical learning theory are well-studied and "more or less solved," while others are just beginning to be investigated. We would like to highlight the following two areas:

Interactive and interpretable machine learning. Here we ask how a fruitful interaction between machine learning algorithms and human users can be achieved. This is clearly a question of rising importance: machine learning systems get more and  more complex and involved, which makes it hard to judge the meaning, implications, and trustworthiness of a machine's inference result. On the other hand, machine learning systems start to have serious impact on every-day life, hence being able to control their results gets more important. The question about interactive and interpretable machine learning clearly has aspects of human-computer interface, but also raises lots of algorithmic and also theoretical issues.

Machine learning for scientific environments. While machine learning methods have been used since more than a decade in some areas of science, for example in bioinformatics or the neurosciences, we currently observe a rising trend to use machine learning methods in many diverse scientific areas, ranging from social sciences over physics to geoscience. When machine learning methods are used in scientific contexts, it is of highest importance to have reliable statistical guarantees. We work towards such guarantees, for example in the field of network science.