ML4AAD is ...

... a research group on Machine Learning for Automated Algorithm Design. We are dealing with meta-algorithmic problems, such as automatic (hyper-) parameter optimization of algorithms and automatic selection of algorithms for instances at hand. We apply our state-of-the-art tools to different domains, such as machine learning, mixed integer programming or satisfiability solving.


Sequential Model-based Algorithm Configuration is a state-of-the-art tool to optimize the performance of your algorithm by determining a well-performing parameter setting.

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Auto-Sklearn is an automated machine learning toolkit to automatically determine a well-performing machine learning pipeline. It is a drop-in replacement for a scikit-learn estimator

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Robust Bayesian Optimization is a framework with state-of-the-art techniques to optimize hyperparameters of machine learning algorithms (including SVMs and Deep Neuronal Networks)

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