Statistical Learning from Dependent Data: Learning Theory, Robust Algorithms, and Applications

• Applicant: Professor Dr. Marius Kloft
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2015 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 266702577

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).

• DFG Programme: Independent Junior Research Groups
• International Connection: Spain, United Kingdom, USA
• Cooperation Partners: Professor Peter L. Bartlett, Ph.D.; Professor Arthur Gretton, Ph.D.; Professor Mehryar Mohri, Ph.D.; Professor Arcadi Navarro, Ph.D.; Professor Dr. Gunnar Rätsch