Junior Research Group SALE
Schnelle Algorithmen für transparente Empfehlungssysteme
engl.: Fast Algorithms for Explainable Recommendation Systems
Technische Universität Chemnitz
Faculty of Mathematics
Idea of the project
The ongoing digitization of all aspects of our society goes hand in hand with the generation of data on an enormous scale.
Commonly, the recorded data sets consist of many data points, each of which is characterized by a large number of characteristics, the so-called features.
Furthermore, the data can also be characterized by uncertainties.
The task of analyzing this data in a reasonable fashion and efficiently extracting required information is of enormous importance in many applications.
With the growing storage of recorded data, the requirements for procedures for their meaningful and transparent analysis is also increasing, of course.
In particular the traceability of the analysis results is only partially or not even given at all by many known AI processes.
The project addresses precisely this point and aims at developing efficient algorithms for data analysis that ensure the traceability of the obtained analysis results based on the underlying data.
SubprojectsSubproject 1: Learning with high-dimensional additive models
Cooperation with the Professorship for Applied Functional Analysis (Prof. Daniel Potts, TU Chemnitz)
Subproject 2: Fast Approximation for Large-Scale Learning
Cooperation with the Professorship for Scientific Computing (Prof. Martin Stoll, TU Chemnitz)
Subproject 3: Large-scale optimization in image processing
Cooperation with the Professorship for Applied Mathematics (Prof. Gabriele Steidl, TU Berlin)