TNT logo Zur zentralen Website der Leibniz Universität Hannover Bannerlogo des Instituts für Informationsveratbeitung

Automated Machine Learning

TNT members involved in this project:
MSc. Theresa Eimer
Prof. Dr. rer. nat. Marius Lindauer

To use machine learning (ML), users have to choose between many design options: (i) ML algorithms (ii) pre-processing techniques, (iii) post-processing techniques, (iv) hyperparameter settings, (v) architectures of neural networks and so on. These design decisions are often responsible whether ML systems return random predictions or achieve state-of-the-art performance. Unfortunately, even for ML-experts it is a tedious and error-prone task and thus it is not easy to make these decisions efficiently.

Automated machine learning (AutoML) addresses this challenge by automating the design process such that even non-ML experts can efficiently develop new ML applications. 

Show all publications
  • Andre Biedenkapp, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, Marius Lindauer
    Algorithm Control: Foundation of a New Meta-Algorithmic Framework
    Proceedings of the European Conference on Artificial Intelligence (ECAI), 2020
  • Marius Lindauer and Frank Hutter
    Best Practices for Scientific Research on Neural Architecture Search
    Arxiv/CoRR, September 2019
  • M. Lindauer and K. Eggensperger and M. Feurer and A. Biedenkapp and J. Marben and P. M\"uller and F. Hutter
    BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
    arXiv:1908.06756 [cs.LG], August 2019