Postdoctoral Research Fellow
Matthias Feurer is a PostDoc at the Machine Learning Lab at the University of Freiburg, Germany. His research focuses on automated machine learning, hyperparameter optimization and meta-learning. He is actively involved in developing open source software for AutoML and is the maintainer and founder of Auto-sklearn and OpenML-Python. Matthias is a founding member of the Open Machine Learning Foundation, gave AutoML tutorials at the GCPR and ECMLPKDD summer school and an invited talk at the AutoML workshop in 2021. Furthermore, he co-organized the AutoML workshops in 2019 and 2020, the 1st AutoML fall school in 2021, and was one of the social chairs of the 1st AutoML conference in 2022. Last, he was part of the winning team of the 1st&2nd AutoML challenges and the BBO challenge@NeurIPS 2020.
You can find more information on my LinkedIn.
- auto-sklearn Automated machine learning
- OpenML interface in python Python API to work with OpenML
- SMAC3 a reimplementation of the SMAC package in python
- HPOBench Hyperparameter Optimization benchmarks library
- HPOlib 1.5 2nd iteration of the Hyperparameter Optimization library, discontinued
- HPOlib Hyperparameter Optimization library, discontinued
- 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 have given 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
In: arXiv:2212.04183 [cs.LG], 2022.
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning Journal Article
In: Journal of Machine Learning Research, vol. 23, no. 261, pp. 1-61, 2022.
University of Freiburg, Department of Computer Science, 2022.
In: Journal of Machine Learning Research (JMLR) -- MLOSS, vol. 23, no. 54, pp. 1-9, 2022.
Practical Transfer Learning for Bayesian Optimization Journal Article
In: arXiv:1802:02219v3 [stat.ML], 2022.
Sixth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems, 2022.
OpenML Benchmarking Suites Inproceedings
In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021.
In: Vanschoren, J.; Yeung, S. (Ed.): Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021.
OpenML-Python: an extensible Python API for OpenML Journal Article
In: Journal of Machine Learning Research, vol. 22, no. 100, pp. 1-5, 2021.
OpenML Benchmarking Suites Journal Article
In: arXiv, vol. 1708.0373v2, pp. 1-6, 2019.
In: arXiv:1908.06756 [cs.LG], 2019.
In: IJCAI 2019 DSO Workshop, 2019.
Hyperparameter Optimization Incollection
In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Sytems, Challenges, pp. 3–33, Springer, 2019.
In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Systems, Challenges, pp. 113–134, Springer, 2019.
Towards Automatically-Tuned Deep Neural Networks Incollection
In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Sytems, Challenges, pp. 135–149, Springer, 2019.
In: ICML 2018 AutoML Workshop, 2018, (This publication is superseded by the 2022 arXiv preprint Practical Transfer Learning for Bayesian Optimization.).
In: ICML 2018 AutoML Workshop, 2018.
Towards Further Automation in AutoML Inproceedings
In: ICML 2018 AutoML Workshop, 2018.
OpenML Benchmarking Suites and the OpenML100 Journal Article
In: arXiv, vol. 1708.0373v1, pp. 1-6, 2017.
Towards Automatically-Tuned Neural Networks Inproceedings
In: ICML 2016 AutoML Workshop, 2016.
Efficient and Robust Automated Machine Learning Inproceedings
In: Advances in Neural Information Processing Systems 28 (NeurIPS'15), pp. 2962–2970, 2015.
Methods for Improving Bayesian Optimization for AutoML Inproceedings
In: ICML 2015 AutoML Workshop, 2015.
In: ICML 2015 MLOSS Workshop, 2015.
In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
In: ECAI workshop on Metalearning and Algorithm Selection (MetaSel), pp. 3–10, 2014, (Superseeded by the AAAI15 paper _Initializing Bayesian Hyperparameter Optimization via Meta-Learning_).
In: NeurIPS workshop on Bayesian Optimization in Theory and Practice, 2013, (Software and benchmarks are available from our HPOlib website.).