Frank Hutter

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Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchVerified email at ai.uni-hannover.de
Ilya LoshchilovNVIDIAVerified email at nvidia.com
Katharina EggenspergerEarly Career Group Leader "AutoML for Science" | University of TübingenVerified email at uni-tuebingen.de
Matthias FeurerLMU MunichVerified email at stat.uni-muenchen.de
Aaron KleinScaDS.AIVerified email at amazon.com
Stefan FalknerBosch Corporate ResearchVerified email at de.bosch.com
Lin XuNETINT, University of British ColumbiaVerified email at cs.ubc.ca
Tonio BallNeuromedical AI Lab Uni Freiburg, NeuroMentum AI (CEO)Verified email at uniklinik-freiburg.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergVerified email at utn.de
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andVerified email at donders.ru.nl
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Thomas StützleF.R.S.-FNRS Research Director, Université Libre de Bruxelles (ULB)Verified email at ulb.ac.be
Kevin MurphyResearch Scientist, GoogleVerified email at google.com
Manuel BlumUniversity of FreiburgVerified email at informatik.uni-freiburg.de
Nando de FreitasCIFAR & DeepMindVerified email at google.com
Philipp HennigUniversity of TübingenVerified email at uni-tuebingen.de

Frank Hutter

ELLIS Institute Tübingen & Professor, University of Freiburg, Germany

Verified email at cs.uni-freiburg.de - Homepage

AutoML Meta-Learning Deep Learning Tabular Data Neural Architecture Search


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Decoupled weight decay regularization I Loshchilov, F Hutter ICLR 2019, 2017	26259	2017
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter ICLR 2017, 2016	9769	2016
Neural architecture search: A survey T Elsken, JH Metzen, F Hutter Journal of Machine Learning Research 20 (55), 1-21, 2019	3593	2019
Sequential model-based optimization for general algorithm configuration F Hutter, HH Hoos, K Leyton-Brown Learning and Intelligent Optimization: 5th International Conference, LION 5 …, 2011	3430	2011
Deep learning with convolutional neural networks for EEG decoding and visualization RT Schirrmeister, JT Springenberg, LDJ Fiederer, M Glasstetter, ... Human brain mapping 38 (11), 5391-5420, 2017	3149	2017
Efficient and robust automated machine learning M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter Advances in neural information processing systems 28, 2015	3051	2015
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	2300	2019
Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms C Thornton, F Hutter, HH Hoos, K Leyton-Brown Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013	2152	2013
Hyperparameter optimization M Feurer, F Hutter Automated machine learning: Methods, systems, challenges, 3-33, 2019	1841	2019
BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter International conference on machine learning, 1437-1446, 2018	1390	2018
ParamILS: an automatic algorithm configuration framework F Hutter, HH Hoos, K Leyton-Brown, T Stützle Journal of artificial intelligence research 36, 267-306, 2009	1309	2009
SATzilla: portfolio-based algorithm selection for SAT L Xu, F Hutter, HH Hoos, K Leyton-Brown Journal of artificial intelligence research 32, 565-606, 2008	1192	2008
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown Journal of Machine Learning Research 18 (25), 1-5, 2017	1006	2017
Bayesian optimization in a billion dimensions via random embeddings Z Wang, F Hutter, M Zoghi, D Matheson, N De Feitas Journal of Artificial Intelligence Research 55, 361-387, 2016	895	2016
Nas-bench-101: Towards reproducible neural architecture search C Ying, A Klein, E Christiansen, E Real, K Murphy, F Hutter International conference on machine learning, 7105-7114, 2019	859	2019
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves T Domhan, JT Springenberg, F Hutter Twenty-fourth international joint conference on artificial intelligence, 2015	825	2015
Fast bayesian optimization of machine learning hyperparameters on large datasets A Klein, S Falkner, S Bartels, P Hennig, F Hutter Artificial intelligence and statistics, 528-536, 2017	758	2017
A downsampled variant of imagenet as an alternative to the cifar datasets P Chrabaszcz, I Loshchilov, F Hutter arXiv preprint arXiv:1707.08819, 2017	719	2017
Efficient multi-objective neural architecture search via lamarckian evolution T Elsken, JH Metzen, F Hutter arXiv preprint arXiv:1804.09081, 2018	668	2018
An efficient approach for assessing hyperparameter importance F Hutter, H Hoos, K Leyton-Brown International conference on machine learning, 754-762, 2014	659	2014

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