PhD Student

Matthias Feurer

Postal address
Institut für Informatik
Albert-Ludwigs-Universität Freiburg
Sekretariat Hutter/Maschinelles Lernen
Georges-Köhler-Allee 074
79110 Freiburg, Germany
Fax
+49 761 203-74217
Office
Building 74, Room 00-010
TwitterLinkedInGoogleScholarORCIDGitHubMarker

Short Bio

Matthias Feurer is a doctoral candidate 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 co-organized the AutoML workshop in 2019 and 2020. Furthermore, 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.

Projects

Discontinued

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

Other

  • 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

2021

Feurer, Matthias; Eggensperger, Katharina; Falkner, Stefan; Lindauer, Marius; Hutter, Frank

Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning Journal Article

In: arXiv:2007.04074, 2021.

Feurer, Matthias; van Rijn, Jan N; Kadra, Arlind; Gijsbers, Pieter; Mallik, Neeratyoy; Ravi, Sahithya; Müller, Andreas; Vanschoren, Joaquin; Hutter, Frank

OpenML-Python: an extensible Python API for OpenML Journal Article

In: Journal of Machine Learning Research, 22 (100), pp. 1-5, 2021.

2020

Awad, Noor; Shala, Gresa; Deng, Difan; Mallik, Neeratyoy; Feurer, Matthias; Eggensperger, Katharina; Biedenkapp, André; Vermetten, Diederick; Wang, Hao; Doerr, Carola; Lindauer, Marius; Hutter, Frank

Squirrel: A Switching Hyperparameter Optimizer Description of the entry by AutoML.org & IOHprofiler to the NeurIPS 2020 BBO challenge Journal Article

In: arXiv:2012.08180 [cs.LG], 2020, (Optimizer description for the NeurIPS 2020 BBO competition. Squirrel won the competition´s warm-starting friendly leaderboard.).

2019

Bischl, Bernd; Casalicchio, Giuseppe; Feurer, Matthias; Hutter, Frank; Lang, Michel; Mantovani, Rafael G; van Rijn, Jan N; Vanschoren, Joaquin

OpenML Benchmarking Suites Journal Article

In: arXiv, 1708.0373v2 , pp. 1-6, 2019.

Lindauer, Marius; Feurer, Matthias; Eggensperger, Katharina; Biedenkapp, André; Hutter, Frank

Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters Inproceedings

In: IJCAI 2019 DSO Workshop, 2019.

Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Marben, Joshua; Müller, Philipp; Hutter, Frank

BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters Journal Article

In: arXiv:1908.06756 [cs.LG], 2019.

Feurer, Matthias; Klein, Aaron; Eggensperger, Katharina; Springenberg, Jost; Blum, Manuel; Hutter, Frank

Auto-sklearn: Efficient and Robust Automated Machine Learning Incollection

In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Systems, Challenges, pp. 113–134, Springer, 2019.

Feurer, Matthias; Hutter, Frank

Hyperparameter Optimization Incollection

In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Sytems, Challenges, pp. 3–33, Springer, 2019.

Mendoza, Hector; Klein, Aaron; Feurer, Matthias; Springenberg, Jost Tobias; Urban, Matthias; Burkart, Michael; Dippel, Max; Lindauer, Marius; Hutter, Frank

Towards Automatically-Tuned Deep Neural Networks Incollection

In: Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin (Ed.): AutoML: Methods, Sytems, Challenges, pp. 135–149, Springer, 2019.

2018

Feurer, Matthias; Letham, Benjamin; Bakshy, Eytan

Scalable Meta-Learning for Bayesian Optimization using Ranking-Weighted Gaussian Process Ensembles Inproceedings

In: ICML 2018 AutoML Workshop, 2018.

Feurer, Matthias; Eggensperger, Katharina; Falkner, Stefan; Lindauer, Marius; Hutter, Frank

Practical Automated Machine Learning for the AutoML Challenge 2018 Inproceedings

In: ICML 2018 AutoML Workshop, 2018.

Feurer, M; Hutter, F

Towards Further Automation in AutoML Inproceedings

In: ICML 2018 AutoML Workshop, 2018.

2017

Bischl, Bernd; Casalicchio, Giuseppe; Feurer, Matthias; Hutter, Frank; Lang, Michel; Mantovani, Rafael G; van Rijn, Jan N; Vanschoren, Joaquin

OpenML Benchmarking Suites and the OpenML100 Journal Article

In: arXiv, 1708.0373v1 , pp. 1-6, 2017.

2016

Mendoza, H; Klein, A; Feurer, M; Springenberg, J; Hutter, F

Towards Automatically-Tuned Neural Networks Inproceedings

In: ICML 2016 AutoML Workshop, 2016.

2015

Feurer, Matthias; Klein, Aaron; Eggensperger, Katharina; Springenberg, Jost Tobias; Blum, Manuel; Hutter, Frank

Efficient and Robust Automated Machine Learning Inproceedings

In: Advances in Neural Information Processing Systems 28 (NeurIPS'15), pp. 2962–2970, 2015.

Feurer, Matthias; Klein, Aaron; Eggensperger, Katharina; Springenberg, Jost Tobias; Blum, Manuel; Hutter, Frank

Methods for Improving Bayesian Optimization for AutoML Inproceedings

In: ICML 2015 AutoML Workshop, 2015.

Vanschoren, J; van Rijn, J; Bischl, B; Casalicchio, G; Lang, M; Feurer, M

OpenML: a Networked Science Platform for Machine Learning (Abstract) Inproceedings

In: ICML 2015 MLOSS Workshop, 2015.

Feurer, M; Springenberg, T; Hutter, F

Initializing Bayesian Hyperparameter Optimization via Meta-Learning Inproceedings

In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.

2014

Feurer, M; Springenberg, T; Hutter, F

Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters Inproceedings

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_).

2013

Eggensperger, Katharina; Feurer, Matthias; Hutter, Frank; Bergstra, James; Snoek, Jasper; Hoos, Holger H; Leyton-Brown, Kevin

Towards an Empirical Foundation for Assessing Bayesian Optimization of Hyperparameters Inproceedings

In: NeurIPS workshop on Bayesian Optimization in Theory and Practice, 2013, (Software and benchmarks are available from our HPOlib website.).