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)Verified email at tnt.uni-hannover.de
Katharina EggenspergerResearcher, Machine Learning Lab, University of FreiburgVerified email at informatik.uni-freiburg.de
Aaron KleinAWS Research BerlinVerified email at cs.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
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Wolfram BurgardProfessor of Computer Science, University of FreiburgVerified email at informatik.uni-freiburg.de
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
Richard DeardenReader in Intelligent Robotics, University of BirminghamVerified email at cs.bham.ac.uk
Nando de FreitasCIFAR & DeepMindVerified email at google.com
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	4886*	2017
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter arXiv preprint arXiv:1608.03983, 2016	3507	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	2404	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	1832	2015
Neural architecture search: A survey T Elsken, JH Metzen, F Hutter The Journal of Machine Learning Research 20 (1), 1997-2017, 2019	1698	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	1451	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	1418	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	1100	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	990	2008
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	936	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	675	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	675	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	668	2017
BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter International Conference on Machine Learning, 1437-1446, 2018	667	2018
Hyperparameter optimization M Feurer, F Hutter Automated machine learning, 3-33, 2019	627	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	567	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	485	2017
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014	471	2014
Initializing Bayesian Hyperparameter Optimization via Meta-Learning. M Feurer, JT Springenberg, F Hutter AAAI, 1128-1135, 2015	449*	2015
Efficient multi-objective neural architecture search via lamarckian evolution T Elsken, JH Metzen, F Hutter arXiv preprint arXiv:1804.09081, 2018	382	2018

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