Can Fairness be Automated?

Posted on April 3, 2023 by Hilde Weerts, Florian Pfisterer, Matthias Feurer, Katharina Eggensperger, Eddie Bergman, Noor Awad, Mykola Pechenizkiy, Joaquin Vanschoren, Bernd Bischl, Frank Hutter

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 […]

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Posted on October 11, 2022 by Samuel Müller, Katharina Eggensperger, Frank Hutter

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 […]

HPOBench: Compare Multi-fidelity Optimization Algorithms with Ease

Posted on September 17, 2021 by Katharina Eggensperger

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 […]

Auto-Sklearn – What happened in 2020

Posted on February 26, 2021 by Katharina Eggensperger, Matthias Feurer

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

Auto-Sklearn 2.0: The Next Generation

Posted on July 13, 2020 by Katharina Eggensperger, Matthias Feurer

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 […]

We did it again: world champions in AutoML

Posted on July 13, 2018 by André Biedenkapp, Katharina Eggensperger, Matthias Feurer, Frank Hutter

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.