… 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, and the Leibniz University of Hannover, led by Prof. Marius Lindauer, 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. A close collaboration between both groups allows the more than 20 international researchers with diverse backgrounds to quickly push the envelope of what is possible in AutoML. The groups successfully raised public funds from DFG, BMBF, BMWi and ERC, as well as funding from close collaborations with big and small companies, such as Bosch and Aerzen.