… provides methods and processes to
- make machine learning more accessible
- improve efficiency of machine learning systems
- accelerate research and AI application development
Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform manual tasks. As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning, AutoML.
Who we are
AutoML is a major topic in the machine learning community and beyond. To contribute to this field, the academic research groups at the University of Freiburg, led by Prof. Frank Hutter, the Leibniz University of Hannover, led by Prof. Marius Lindauer, and the University of Tübingen, led by Dr. Katharina Eggensperger, develop new state-of-the-art approaches and open-source tools for topics such as hyperparameter optimization, neural architecture search and dynamic algorithm configuration. The first group was founded in 2013 by Prof. Hutter as an Emmy-Noether research group, where Prof. Lindauer joined in 2014 as a postdoc, before founding his own group in 2019 in Hannover. In 2023, Dr. Eggensperger, who did her Ph.D. under the supervision of Prof. Hutter and Prof. Lindauer, founded her own early career research group at the University of Tübingen. A close collaboration between the three groups allows the roughly 30 international researchers to push the frontier of AutoML. The groups raised a total of 4 ERC grants (ERC Starting, Proof of Concept, and Consolidator grants for Frank Hutter, and ERC Starting grant for Marius Lindauer), further public funding from DFG, BMBF, BMWK, BMUV, as well as major funding from close collaborations with big and small companies, such as Bosch and Aerzen.