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Postdoctoral Research Fellow

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 00-012
TwitterLinkedInGoogleScholarORCIDGitHubMarker

Short Bio

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.

Projects

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 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

Publications

2022

Feurer, Matthias; Eggensperger, Katharina; Bergman, Edward; Pfisterer, Florian; Bischl, Bernd; Hutter, Frank

Mind the Gap: Measuring Generalization Performance Across Multiple Objectives Journal Article

In: arXiv:2212.04183 [cs.LG], 2022.

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

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.

Feurer, Matthias

Robust and Efficient Automated Machine Learning: Systems, Infrastructure and Advances in Hyperparameter Optimization PhD Thesis

University of Freiburg, Department of Computer Science, 2022.

Lindauer, Marius; Eggensperger, Katharina; Feurer, Matthias; Biedenkapp, André; Deng, Difan; Benjamins, Carolin; Ruhkopf, Tim; Sass, René; Hutter, Frank

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization Journal Article

In: Journal of Machine Learning Research (JMLR) -- MLOSS, vol. 23, no. 54, pp. 1-9, 2022.

Feurer, Matthias; Letham, Benjamin; Hutter, Frank; Bakshy, Eytan

Practical Transfer Learning for Bayesian Optimization Journal Article

In: arXiv:1802:02219v3 [stat.ML], 2022.

Müller, Samuel; Arango, Sebastian Pineda; Feurer, Matthias; Grabocka, Josif; Hutter, Frank

Bayesian Optimization with a Neural Network Meta-learned on Synthetic Data Only Workshop

Sixth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems, 2022.

2021

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

OpenML Benchmarking Suites Inproceedings

In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021.

Eggensperger, Katharina; Müller, Philipp; Mallik, Neeratyoy; Feurer, Matthias; Sass, René; Klein, Aaron; Awad, Noor; Lindauer, Marius; Hutter, Frank

HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO Inproceedings

In: Vanschoren, J.; Yeung, S. (Ed.): Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 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, vol. 22, no. 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, vol. 1708.0373v2, pp. 1-6, 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.

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.

Feurer, Matthias; Hutter, Frank

Hyperparameter Optimization Incollection

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

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, (This publication is superseded by the 2022 arXiv preprint Practical Transfer Learning for Bayesian Optimization.).

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, vol. 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.).