# Prof. Dr. Matthias Feurer

# About

I am a Thomas Bayes Fellow and substitute professor at the Chair of Statistical Learning and Data Science funded by the Munich Center for Machine Learning.

With my research, I aim to simplify the usage of machine learning by researching methods and developing tools that allow the usage of machine learning by domain scientists and also make machine learning more efficient for expert users. My focus is on Automated Machine Learning (AutoML) which encompasses methods for hyperparameter optimization, meta-learning, and model selection. However, machine learning is not solely about predictive performance but also about additional objectives such as model interpretability, deployability, or fairness. For this, I want to move beyond performance-driven machine learning by applying multi-objective AutoML.

Furthermore, I am developing and contributing to multiple open-source projects in the realm of AutoML (see below) and am a co-founder of the Open Machine Learning Foundation that supports the development of OpenML.org.

# Short Bio

Since April 2023, Matthias Feurer is a Thomas Bayes Fellow and substitute professor, funded by the Munich Center of Machine Learning (MCML). He is a member of the Chair of Statistical Learning and Data Science (Department of Statistics, LMU Munich), which is headed by Prof. Dr. Bernd Bischl. Previously, he was a PostDoc at the machine learning lab at the Albert-Ludwigs-Universität Freiburg, where he also finished his PhD under the supervision of Prof. Dr. Frank Hutter on the topic of Automated Machine Learning. Before that, he studied Computer Science at the University of Freiburg (M.Sc.) and Computer Science and Media at the Media University Stuttgart (B.Sc.).

# Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

matthias.feurer [at] stat.uni-muenchen.de

# Teaching

- Winter Term 2023/2024
- Summer Term 2023

# Thesis supervision

Please see the AutoML thesis topics on Moodle.

# Research Interests

My main research interest lies in the field of Automated Machine Learning (AutoML):

- AutoML Systems
- Hyperparameter Optimization
- Bayesian Optimization
- Meta-Learning

Other topics that interest me:

- Empirical Machine Learning
- Open Source Software

# You Can Find me on

# Projects

- auto-sklearn: An automated machine learning solution that simplifies solving supervised classification and regression problems.
- OpenML-Python: Python API to interact with OpenML.org
- SMAC3: a reimplementation of the SMAC method in Python
- HPOBench: Hyperparameter Optimization benchmarks library

## Discontinued

- HPOlib 1.5: 2nd iteration of the Hyperparameter Optimization library, discontinued
- HPOlib: Hyperparameter Optimization library, discontinued

# Other

- I gave an invited talk at the AutoML workshop describing our work on Auto-sklearn 2.0 which can be found on Slideslive.
- Together with Katharina I gave an introduction to Auto-sklearn at the EuroPython 2021 which can be found on Youtube.
- We placed 1st in the warmstarting-friendly leaderboard of the BBO NeurIPS challenge.
- We wrote a blog post about meta-learning the arguments to Auto-sklearn, check it out here.
- Read our blog post on winning the 2nd AutoML challenge.
- Read our winning blog post of the kdnuggets blog contest on AutoML.
- Check out this interview with Aaron, Frank and me about the winning the AutoML challenge.
- Our work on Auto-sklearn got covered in the Computer Vision News.

# Publications

- Fischer S, Harutyunyan L, Feurer M, Bischl B (2023) OpenML-CTR23 – A curated tabular regression benchmarking suite
*AutoML Conference 2023 (Workshop)*,

link|pdf. - Feurer M, Eggensperger K, Bergman E, Pfisterer F, Bischl B, Hutter F (2023) Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
*Advances in Intelligent Data Analysis XXI. IDA 2023.*, pp. 130–142. Springer, Cham. - Weerts H, Pfisterer F, Feurer M, Eggensperger K, Bergman E, Awad N, Vanschoren J, Pechenizkiy M, Bischl B, Hutter F (2023) Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML.
*arXiv:2303.08485 [cs.AI]*. - Müller S, Feurer M, Hollmann N, Hutter F (2023) PFNs Are Flexible Models for Real-World Bayesian Optimization
*Accepted at the International Conference on Machine Learning (ICML)*, - Feurer M, Eggensperger K, Falkner S, Lindauer M, Hutter F (2022) Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning.
*Journal of Machine Learning Research*23, 1–61. - Lindauer M, Eggensperger K, Feurer M, Biedenkapp A, Deng D, Benjamins C, Ruhkopf T, Sass R, Hutter F (2022) SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.
*Journal of Machine Learning Research*23, 1–9. - Feurer M (2022) Robust and Efficient Automated Machine Learning: Systems, Infrastructure and Advances in Hyperparameter Optimization.
- Feurer M, Letham B, Hutter F, Bakshy E (2022) Practical Transfer Learning for Bayesian Optimization.
*arXiv:1802.02219v4 [cs.LG]*. - Bischl B, Casalicchio G, Feurer M, Gijsbers P, Hutter F, Lang M, Mantovani RG, Rijn JN van, Vanschoren J (2021) OpenML Benchmarking Suites
*Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks*, - Eggensperger K, Müller P, Mallik N, Feurer M, Sass R, Klein A, Awad N, Lindauer M, Hutter F (2021) HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
*Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks*, - Feurer M, Rijn JN van, Kadra A, Gijsbers P, Mallik N, Ravi S, Müller A, Vanschoren J, Hutter F (2021) OpenML-Python: an extensible Python API for OpenML.
*Journal of Machine Learning Research*22, 1–5. - Awad N, Shala G, Deng D, Mallik N, Feurer M, Eggensperger K, Biedenkapp A, Vermetten D, Wang H, Doerr C, Lindauer M, Hutter F (2020) Squirrel: A Switching Hyperparameter Optimizer Description of the entry by AutoML.org & IOHprofiler to the NeurIPS 2020 BBO challenge.
*arXiv:2012.08180 [cs.LG]*. - Lindauer M, Eggensperger K, Feurer M, Biedenkapp A, Marben J, Müller P, Hutter F (2019) BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters.
*arXiv:1908.06756 [cs.LG]*. - Lindauer M, Feurer M, Eggensperger K, Biedenkapp A, Hutter F (2019) Towards Assessing the Impact of Bayesian Optimization’s Own Hyperparameters
*IJCAI 2019 Data Science Meets Optimisation Workshop*, - Feurer M, Hutter F (2019) Hyperparameter Optimization
*AutoML: Methods, Sytems, Challenges*, pp. 3–38. Springer. - Feurer M, Klein A, Eggensperger K, Springenberg JT, Blum M, Hutter F (2019) Auto-sklearn: Efficient and Robust Automated Machine Learning
*AutoML: Methods, Sytems, Challenges*, pp. 113–134. Springer. - Mendoza H, Klein A, Feurer M, Springenberg JT, Urban M, Burkart M, Dippel M, Lindauer M, Hutter F (2019) Towards Automatically-Tuned Deep Neural Networks
*AutoML: Methods, Sytems, Challenges*, pp. 135–149. Springer. - Feurer M, Klein A, Eggensperger K, Springenberg JT, Blum M, Hutter F (2015) Efficient and Robust Automated Machine Learning
*Advances in Neural Information Processing Systems*, - Feurer M, Springenberg JT, Hutter F (2015) Initializing Bayesian Hyperparameter Optimization via Meta-Learning
*Proceedings of the AAAI Conference on Artificial Intelligence*, - Vanschoren J, Rijn JN van, Bischl B, Casalicchio G, Feurer M (2015) OpenML: a networked science platform for machine learning
*ICML Workshop on Machine Learning Open Source Software (MLOSS 2015)*, - Eggensperger K, Feurer M, Hutter F, Bergstra J, Snoek J, Hoos HH, Leyton-Brown K (2013) Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters
*NeurIPS workshop on Bayesian Optimization in Theory and Practice*,