Matthias Feurer

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Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Katharina EggenspergerResearcher, University of TübingenVerified email at uni-tuebingen.de
Jost Tobias SpringenbergAlbert-Ludwigs-Universität FreiburgVerified email at informatik.uni-freiburg.de
Aaron KleinAWS Research BerlinVerified email at amazon.com
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence, L3S Research CenterVerified email at ai.uni-hannover.de
Manuel BlumUniversity of FreiburgVerified email at informatik.uni-freiburg.de
Stefan FalknerBosch Center for Artificial IntelligenceVerified email at de.bosch.com
André BiedenkappUniversity of FreiburgVerified email at cs.uni-freiburg.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Eytan BakshyResearch Director, MetaVerified email at fb.com
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
James BergstraVerified email at uwaterloo.ca
Jasper SnoekGoogle BrainVerified email at google.com
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Pieter GijsbersEindhoven University of TechnologyVerified email at tue.nl
Neeratyoy MallikPhD student, AutoML FreiburgVerified email at cs.uni-freiburg.de
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningVerified email at campus.lmu.de

Matthias Feurer

Statistical Learning and Data Science, Department of Statistics, LMU Munich & Munich Center for

Verified email at stat.uni-muenchen.de - Homepage

Hyperparameter optimization Meta-learning Bayesian optimization OpenML AutoML


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Efficient and Robust Automated Machine Learning M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter Advances in Neural Information Processing Systems, 2962-2970, 2015	2184	2015
Hyperparameter Optimization M Feurer, F Hutter AutoML: Methods, Sytems, Challenges, 3-37, 2019	973	2019
Initializing bayesian hyperparameter optimization via meta-learning M Feurer, J Springenberg, F Hutter Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015	526*	2015
Towards an empirical foundation for assessing bayesian optimization of hyperparameters K Eggensperger, M Feurer, F Hutter, J Bergstra, J Snoek, H Hoos, ... NIPS workshop on Bayesian Optimization in Theory and Practice, 1-5, 2013	401	2013
Towards Automatically-Tuned Deep Neural Networks H Mendoza, A Klein, M Feurer, JT Springenberg, M Urban, M Burkart, ... Automated Machine Learning, 135-149, 2019	300*	2019
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter Journal of Machine Learning Research 23 (261), 1-61, 2022	200*	2022
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ... Journal of Machine Learning Research 23 (54), 1-9, 2022	169*	2022
OpenML Benchmarking Suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021	112*	2021
Practical Transfer Learning for Bayesian Optimization M Feurer, B Letham, F Hutter, E Bakshy arXiv:1802.02219v3, 2022	111*	2022
Practical Automated Machine Learning for the AutoML Challenge 2018 M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter ICML 2018 AutoML Workshop, 2018	87	2018
OpenML-Python: an extensible Python API for OpenML M Feurer, JN van Rijn, A Kadra, P Gijsbers, N Mallik, S Ravi, A Müller, ... Journal of Machine Learning Research 22 (100), 1-5, 2021	56	2021
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, J Marben, ... arXiv preprint arXiv:1908.06756, 2019	36	2019
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO K Eggensperger, P Müller, N Mallik, M Feurer, R Sass, A Klein, N Awad, ... Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021	33	2021
Towards Further Automation in AutoML M Feurer, F Hutter ICML 2018 AutoML Workshop, 2018	26	2018
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters M Lindauer, M Feurer, K Eggensperger, A Biedenkapp, F Hutter arXiv preprint arXiv:1908.06674, 2019	13	2019
Squirrel: A Switching Hyperparameter Optimizer N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ... arXiv preprint arXiv:2012.08180, 2020	3	2020
OpenML: a networked science platform for machine learning J Vanschoren, JN van Rijn, B Bischl, G Casalicchio, M Lang, M Feurer ICML 2015 MLOSS Workshop 3, 2015	3	2015
PFNs Are Flexible Models for Real-World Bayesian Optimization S Müller, M Feurer, N Hollmann, F Hutter arXiv preprint arXiv:2305.17535, 2023		2023
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives M Feurer, K Eggensperger, E Bergman, F Pfisterer, B Bischl, F Hutter Advances in Intelligent Data Analysis XXI: 21st International Symposium on …, 2023		2023
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML H Weerts, F Pfisterer, M Feurer, K Eggensperger, E Bergman, N Awad, ... arXiv:2303.08485, 2023		2023

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