Frank Hutter

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Citations	33128	28471
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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)Verified email at tnt.uni-hannover.de
Aaron KleinAWS Research BerlinVerified email at cs.uni-freiburg.de
Katharina EggenspergerPhD Student, Machine Learning Lab, University of FreiburgVerified email at informatik.uni-freiburg.de
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 BallHead of Neuromedical AI Lab FreiburgVerified email at uniklinik-freiburg.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Thomas StützleF.R.S.-FNRS Research Director, Université Libre de Bruxelles (ULB)Verified email at ulb.ac.be
Wolfram BurgardProfessor of Computer Science, University of FreiburgVerified email at informatik.uni-freiburg.de
Kevin MurphyResearch Scientist, GoogleVerified email at google.com
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andVerified email at donders.ru.nl
Manuel BlumUniversity of FreiburgVerified email at informatik.uni-freiburg.de
Richard DeardenReader in Intelligent Robotics, University of BirminghamVerified email at cs.bham.ac.uk
Nando de FreitasCIFAR & DeepMindVerified email at google.com

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	3883*	2017
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter arXiv preprint arXiv:1608.03983, 2016	3048	2016
Sequential model-based optimization for general algorithm configuration F Hutter, HH Hoos, K Leyton-Brown International conference on learning and intelligent optimization, 507-523, 2011	2286	2011
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	1688	2015
Neural architecture search: A survey T Elsken, JH Metzen, F Hutter The Journal of Machine Learning Research 20 (1), 1997-2017, 2019	1536	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	1390	2013
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	1299	2017
ParamILS: an automatic algorithm configuration framework F Hutter, HH Hoos, K Leyton-Brown, T Stützle Journal of Artificial Intelligence Research 36, 267-306, 2009	1074	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	968	2008
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	828	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	641	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	641	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	634	2017
BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter International Conference on Machine Learning, 1437-1446, 2018	600	2018
Hyperparameter optimization M Feurer, F Hutter Automated machine learning, 3-33, 2019	541	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	531	2015
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014	457	2014
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	444	2017
Initializing Bayesian Hyperparameter Optimization via Meta-Learning. M Feurer, JT Springenberg, F Hutter AAAI, 1128-1135, 2015	429*	2015
Automatic algorithm configuration based on local search F Hutter, HH Hoos, T Stützle Aaai 7, 1152-1157, 2007	371	2007

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