At the risk of sounding cliché, “with great power comes great responsibility.” While we don’t want to suggest that machine learning (ML) practitioners are superheroes, what was true for Spiderman is also true for those building predictive models – and even more so for those building AutoML tools. Only last year, the Netherlands Institute for […]

A radically new approach to tabular classification: we introduce TabPFN, a new tabular data classification method that takes < 1 second & yields SOTA performance (competitive with the best AutoML pipelines in an hour). So far, it is limited in scale, though: it can only tackle problems up to 1000 training examples, 100 features and […]

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 […]

2020 is over. Time to look back at the amazing major features we introduced to Auto-Sklearn.

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 […]

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.