Frank Hutter

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Holger HoosLeiden University, The Netherlands & University of British Columbia, CanadaVerified email at liacs.nl
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
Jost Tobias SpringenbergAlbert-Ludwigs-Universität FreiburgVerified email at informatik.uni-freiburg.de
Stefan FalknerBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Ilya LoshchilovVerified email at inria.fr
Lin XuUniversity of British ColumbiaVerified email at cs.ubc.ca
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Tonio BallHead of Neuromedical AI Lab FreiburgVerified email at uniklinik-freiburg.de
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
Manuel BlumUniversity 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
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
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	1904	2011
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter arXiv preprint arXiv:1608.03983, 2016	1853	2016
Decoupled weight decay regularization I Loshchilov, F Hutter arXiv preprint arXiv:1711.05101, 2017	1681*	2017
Auto-sklearn: efficient and robust automated machine learning M Feurer, A Klein, K Eggensperger, JT Springenberg, M Blum, F Hutter Automated Machine Learning, 113-134, 2019	1280	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	1165	2013
Neural architecture search: A survey T Elsken, JH Metzen, F Hutter The Journal of Machine Learning Research 20 (1), 1997-2017, 2019	984	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	971	2009
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	922	2017
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	886	2008
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	521	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	509	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	509	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	506	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	425	2015
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014	399	2014
BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter International Conference on Machine Learning, 1437-1446, 2018	384	2018
Initializing Bayesian Hyperparameter Optimization via Meta-Learning. M Feurer, JT Springenberg, F Hutter AAAI, 1128-1135, 2015	349*	2015
Automatic algorithm configuration based on local search F Hutter, HH Hoos, T Stützle Aaai 7, 1152-1157, 2007	344	2007
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	343	2017
Hyperparameter optimization M Feurer, F Hutter Automated machine learning, 3-33, 2019	304	2019

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