HPOBench: Compare Multi-fidelity Optimization Algorithms with Ease

When researching and developing new hyperparameter optimization (HPO) methods, a good collection of benchmark problems, ideally relevant, realistic and cheap-to-evaluate, is a very valuable resource. While such collections exist for synthetic problems (COCO) or simple HPO problems (Bayesmark), to the best of our knowledge there is no such collection for multi-fidelity benchmarks. With ever-growing machine […]

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Auto-Sklearn 2.0: The Next Generation

Since our initial release of auto-sklearn 0.0.1 in May 2016 and the publication of the NeurIPS paper “Efficient and Robust Automated Machine Learning” in 2015, we have spent a lot of time on maintaining, refactoring and improving code, but also on new research. Now, we’re finally ready to share the next version of our flagship […]

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We did it again: world champions in AutoML

By Our ML Freiburg lab is the world champion in automatic machine learning (AutoML) again! After winning the first international AutoML challenge (2015-2016), we also just won the second international AutoML challenge (2017-2018). Our system PoSH-Auto-sklearn outperformed all other 41 participating AutoML systems.

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