Statistisches Lernen aus abhängigen Daten: Lerntheorie, Robuste Algorithmen und Anwendungen: Statistical learning from dependent data: Learning Theory, Robust Algorithms, and Applications

• Duration: 01.01.2015 - 31.12.2023
  Funding Organization: DFG
  
  Prof. Dr. Marius Kloft
  University of Kaiserslautern-Landau
  
  

  Machine learning constitutes one of the key technologies to thoroughly analyze empirical data. One of the most common assumptions
  in machine learning is that the empirical data is realized from independent random variables. However, in practice
  this assumption can be violated when the data exhibits temporal and spatial dependencies or is recorded under varying experimental
  conditions or confounding factors. With this research program we propose to work toward a theoretically sound and
  general framework of statistical learning from dependent data. At the heart of which lies the development of novel algorithms
  creating learning in particular cases of this settings and their application to problems from the sciences and technology. A particular
  emphasis of the program is on gaining an understanding of the theoretical foundations of learning in dependent settings
  (in order to explain under which circumstances the algorithms will work fine). All algorithms are embedded into a framework of
  automatic and sound interpretation of the trained models in terms of p-values (in order to facilitate further analysis by domain
  experts).