SMAC
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.
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.
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
BOHB combines the benefits of both Bayesian Optimization and HyperBand, in order to achieve the best of both worlds: strong anytime performance and fast convergence to optimal configurations.
You can learn more about us by visiting our university website at ML Freiburg.