Matthias Feurer

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Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Katharina EggenspergerPhD Student, Machine Learning Lab, University of FreiburgVerified email at informatik.uni-freiburg.de
Jost Tobias SpringenbergAlbert-Ludwigs-Universität FreiburgVerified email at informatik.uni-freiburg.de
Aaron KleinAWS Research BerlinVerified email at cs.uni-freiburg.de
Marius LindauerLeibniz University Hannover (Germany)Verified email at tnt.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 BiedenkappPhD candidate, University of FreiburgVerified email at cs.uni-freiburg.de
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Eytan BakshyResearch Director, MetaVerified email at fb.com
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.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
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
Neeratyoy MallikPhD student, AutoML FreiburgVerified email at cs.uni-freiburg.de
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaVerified email at utfpr.edu.br

Matthias Feurer

Machine Learning group, University of Freiburg

Verified email at informatik.uni-freiburg.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	1751	2015
Hyperparameter Optimization M Feurer, F Hutter AutoML: Methods, Sytems, Challenges, 3-37, 2019	589	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	442*	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	342	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	250*	2019
Practical Transfer Learning for Bayesian Optimization M Feurer, B Letham, F Hutter, E Bakshy arXiv:1802.02219v3, 2022	87*	2022
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter	82*	2021
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	81*	2021
Practical Automated Machine Learning for the AutoML Challenge 2018 M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter ICML 2018 AutoML Workshop, 2018	72	2018
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	68*	2022
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	37	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	25	2019
Towards Further Automation in AutoML M Feurer, F Hutter ICML 2018 AutoML Workshop, 2018	23	2018
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	11	2021
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	9	2019
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	4	2015
Squirrel: A Switching Hyperparameter Optimizer N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ... arXiv preprint arXiv:2012.08180, 2020	2	2020

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