Matthias Feurer

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Frank HutterELLIS Institute Tübingen & Professor, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Katharina EggenspergerEarly Career Group Leader "AutoML for Science" | University of TübingenVerified email at uni-tuebingen.de
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchVerified email at ai.uni-hannover.de
Aaron KleinScaDS.AIVerified email at amazon.com
Manuel BlumUniversity of FreiburgVerified email at informatik.uni-freiburg.de
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
André BiedenkappUniversity of FreiburgVerified email at cs.uni-freiburg.de
Stefan FalknerBosch Corporate ResearchVerified email at de.bosch.com
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningVerified email at campus.lmu.de
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Neeratyoy MallikPhD student, AutoML FreiburgVerified email at cs.uni-freiburg.de
Difan DengLeibniz Universität HannoverVerified email at tnt.uni-hannover.de
Eytan BakshyResearch Director, MetaVerified email at fb.com
Pieter GijsbersEindhoven University of TechnologyVerified email at tue.nl
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaVerified email at utfpr.edu.br
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Noor AwadPostdoctoral Research Fellow, University of FreiburgVerified email at cs.uni-freiburg.de
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
James BergstraPrincipal Engineer, Ocado TechnologyVerified email at ocado.com

Matthias Feurer

LMU Munich

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	2884	2015
Hyperparameter Optimization M Feurer, F Hutter AutoML: Methods, Sytems, Challenges, 3-37, 2019	1741	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	659*	2015
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	612*	2022
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	473	2013
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	417*	2022
Towards Automatically-Tuned Deep Neural Networks H Mendoza, A Klein, M Feurer, JT Springenberg, M Urban, M Burkart, ... Automated Machine Learning, 135-149, 2019	369*	2019
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	220*	2021
Practical Transfer Learning for Bayesian Optimization M Feurer, B Letham, F Hutter, E Bakshy arXiv:1802.02219v3, 2022	168*	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	104	2021
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	101	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	52	2019
PFNs4BO: In-Context Learning for Bayesian Optimization S Müller, M Feurer, N Hollmann, F Hutter 40th International Conference on Machine Learning, 2023	28	2023
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	22	2019
Can fairness be automated? guidelines and opportunities for fairness-aware automl H Weerts, F Pfisterer, M Feurer, K Eggensperger, E Bergman, N Awad, ... Journal of Artificial Intelligence Research 79, 639-677, 2024	19	2024
OpenML-CTR23–A curated tabular regression benchmarking suite SF Fischer, M Feurer, B Bischl AutoML Conference 2023 (Workshop), 2023	18	2023
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives M Feurer, K Eggensperger, E Bergman, F Pfisterer, B Bischl, F Hutter International Symposium on Intelligent Data Analysis, 130-142, 2023	7	2023
Position: Why We Must Rethink Empirical Research in Machine Learning M Herrmann, FJD Lange, K Eggensperger, G Casalicchio, M Wever, ... Forty-first International Conference on Machine Learning, 2024	5*	2024
Squirrel: A Switching Hyperparameter Optimizer N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ... arXiv preprint arXiv:2012.08180, 2020	5	2020
Interpretable machine learning for TabPFN D Rundel, J Kobialka, C von Crailsheim, M Feurer, T Nagler, D Rügamer World Conference on Explainable Artificial Intelligence, 465-476, 2024	3	2024

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