Matthias Feurer

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Frank HutterELLIS Institute Tübingen & Professor, University of Freiburg, GermanyPatvirtintas el. paštas cs.uni-freiburg.de
Katharina EggenspergerEarly Career Group Leader "AutoML for Science" | University of TübingenPatvirtintas el. paštas uni-tuebingen.de
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchPatvirtintas el. paštas ai.uni-hannover.de
Aaron KleinScaDS.AIPatvirtintas el. paštas amazon.com
Manuel BlumUniversity of FreiburgPatvirtintas el. paštas informatik.uni-freiburg.de
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichPatvirtintas el. paštas stat.uni-muenchen.de
André BiedenkappUniversity of FreiburgPatvirtintas el. paštas cs.uni-freiburg.de
Stefan FalknerBosch Corporate ResearchPatvirtintas el. paštas de.bosch.com
Joaquin VanschorenEindhoven University of TechnologyPatvirtintas el. paštas tue.nl
Giuseppe CasalicchioSenior Lecturer and Researcher, LMU Munich, Munich Center for Machine LearningPatvirtintas el. paštas campus.lmu.de
Jan N. van RijnLeiden UniversityPatvirtintas el. paštas liacs.leidenuniv.nl
Difan DengLeibniz Universität HannoverPatvirtintas el. paštas tnt.uni-hannover.de
Neeratyoy MallikPhD student, AutoML FreiburgPatvirtintas el. paštas cs.uni-freiburg.de
Eytan BakshyResearch Director, MetaPatvirtintas el. paštas fb.com
Pieter GijsbersEindhoven University of TechnologyPatvirtintas el. paštas tue.nl
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaPatvirtintas el. paštas utfpr.edu.br
Michel LangTU Dortmund; LMU MunichPatvirtintas el. paštas statistik.tu-dortmund.de
Noor AwadPostdoctoral Research Fellow, University of FreiburgPatvirtintas el. paštas cs.uni-freiburg.de
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairPatvirtintas el. paštas cs.ubc.ca
James BergstraPrincipal Engineer, Ocado TechnologyPatvirtintas el. paštas ocado.com

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Matthias Feurer

LMU Munich

Patvirtintas el. paštas stat.uni-muenchen.de - Pagrindinis puslapis

Hyperparameter optimization Meta-learning Bayesian optimization OpenML AutoML


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	2998	2015
Hyperparameter Optimization M Feurer, F Hutter AutoML: Methods, Sytems, Challenges, 3-37, 2019	1885	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	674*	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	660*	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	479	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	458*	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	378*	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	244*	2021
Practical Transfer Learning for Bayesian Optimization M Feurer, B Letham, F Hutter, E Bakshy arXiv:1802.02219v3, 2022	178*	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	111	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	104	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	53	2019
PFNs4BO: In-Context Learning for Bayesian Optimization S Müller, M Feurer, N Hollmann, F Hutter 40th International Conference on Machine Learning, 2023	39	2023
OpenML-CTR23–A curated tabular regression benchmarking suite SF Fischer, M Feurer, B Bischl AutoML Conference 2023 (Workshop), 2023	26	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	24	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	21	2024
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	9*	2024
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: A Call to Action for a Human-Centered AutoML Paradigm M Lindauer, F Karl, A Klier, J Moosbauer, A Tornede, A Mueller, F Hutter, ... Accepted at ICML 2024, 2024	6	2024
Squirrel: A Switching Hyperparameter Optimizer N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ... arXiv preprint arXiv:2012.08180, 2020	6	2020

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