Automated Machine Learning

TNT members involved in this project:
Difan Deng, M. Sc.
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 AutoML tools support users to efficiently develop new ML applications.

Hyperparameter Optimization and Bayesian Optimization

To achieve peak-performance with an algorithm, choosing an appropriate hyperparameter configuration is crucial. Since hyperparameters are often not very intuitive for human developers, it is a tedious and error-prone task to choose these settings. Bayesian Optimization is a sample-efficient approach to find such hyperparameter configurations in an automatic way, saving human developers tremendous amounts of development time.

Neural Architecture Search

Applying deep learning to new datasets also requires to find a well-performing architecture of a deep neural network. Such an architecture influences the performance, but also other metrics, such as inference time, memory consumption etc pp. Unfortunately, it is again not obvious for human developers how to design such deep neural networks making the process fairly inefficient. Neural architecture search is an paradigma to automatically determine the best architectures for new datasets, making new applications of deep learning feasible also at larger scale.

Interpretability of AutoML 

A major drawback of AutoML tools is the risk that ML will be even a more mysterious black box than it ever was. Therefore, we also develop analysis tools that provide feedback to AutoML users about important insights, such as, (i) how to use AutoML tools more efficiently or (ii) which hyperparameter decisions were important to achieve the final performance. This helps ML developers to get a better understanding of why and how ML and AutoML works.

Show all publications
  • Berend Denkena, Marc Dittrich, Marius Lindauer, Mainka , Lukas Stürenburg
    Using AutoML to Optimize Shape Error Prediction in Milling Processes
    Proceedings of 20th Machining Innovations Conference for Aerospace Industry (MIC), December 2020
  • Gresa Shala, Andre Biedenkapp, Noor Awad, Steven Adriaensen, Marius Lindauer, Frank Hutter
    Learning Step-Size Adaptation in CMA-ES
    Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}'20), September 2020
  • Katharina Eggensperger, Kai Haase, Philipp Müller, Marius Lindauer, Frank Hutter
    Neural Model-based Optimization with Right-Censored Observations
    CoRR, ArXiv, September 2020