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, L3S Research CenterVerified email at ai.uni-hannover.de
Katharina EggenspergerResearcher, University of TübingenVerified email at uni-tuebingen.de
Aaron KleinAWS Research BerlinVerified email at amazon.com
Matthias FeurerMachine Learning group, University of FreiburgVerified email at informatik.uni-freiburg.de
Ilya LoshchilovVerified email at inria.fr
Stefan FalknerBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Jost Tobias SpringenbergAlbert-Ludwigs-Universität FreiburgVerified email at informatik.uni-freiburg.de
Lin XuUniversity 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
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergVerified email at utn.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Thomas StützleF.R.S.-FNRS Research Director, Université Libre de Bruxelles (ULB)Verified email at ulb.ac.be
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andVerified email at donders.ru.nl
Kevin MurphyResearch Scientist, GoogleVerified email at google.com
Nando de FreitasCIFAR & DeepMindVerified email at google.com
Richard DeardenReader in Intelligent Robotics, University of BirminghamVerified email at cs.bham.ac.uk
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de

Frank Hutter

Professor of Computer Science, University of Freiburg, Germany

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

AutoML Neural Architecture Search Meta-Learning Deep Learning Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Decoupled weight decay regularization I Loshchilov, F Hutter arXiv preprint arXiv:1711.05101, 2017	7374*	2017
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter arXiv preprint arXiv:1608.03983, 2016	4549	2016
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	2592	2011
Neural architecture search: A survey T Elsken, JH Metzen, F Hutter The Journal of Machine Learning Research 20 (1), 1997-2017, 2019	2066	2019
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	2058	2015
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	1688	2017
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	1581	2013
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	1162	2019
ParamILS: an automatic algorithm configuration framework F Hutter, HH Hoos, K Leyton-Brown, T Stützle Journal of Artificial Intelligence Research 36, 267-306, 2009	1142	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	1037	2008
Hyperparameter optimization M Feurer, F Hutter Automated machine learning: Methods, systems, challenges, 3-33, 2019	836	2019
BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter International Conference on Machine Learning, 1437-1446, 2018	808	2018
Auto-WEKA: Automatic model selection and hyperparameter optimization in WEKA L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown Automated Machine Learning, 81-95, 2019	747	2019
Auto-WEKA: Automatic model selection and hyperparameter optimization in WEKA L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown Automated Machine Learning, 81-95, 2019	747	2019
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	747	2017
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	637	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	543	2017
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014	498	2014
Initializing Bayesian Hyperparameter Optimization via Meta-Learning. M Feurer, JT Springenberg, F Hutter AAAI, 1128-1135, 2015	494*	2015
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	468	2019

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