Frank Hutter - Google Scholar Citations

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Citations	13608	10946
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Co-authors

Holger HoosLeiden University, The Netherlands & University of British Columbia, CanadaVerified email at liacs.nl
Kevin Leyton-BrownProfessor of Computer Science, University of British Columbia; Canada CIFAR AI Chair, AmiiVerified email at cs.ubc.ca
Aaron KleinAmazon 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
Marius LindauerLeibniz University Hannover (Germany)Verified email at tnt.uni-hannover.de
Jost Tobias SpringenbergAlbert-Ludwigs-Universität FreiburgVerified email at informatik.uni-freiburg.de
Lin XuUniversity of British ColumbiaVerified email at cs.ubc.ca
Stefan FalknerBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Ilya LoshchilovVerified email at inria.fr
Tonio BallHead of Neuromedical AI Lab FreiburgVerified email at uniklinik-freiburg.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
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Wolfram BurgardProfessor of Computer Science, University of FreiburgVerified email at informatik.uni-freiburg.de
Manuel BlumUniversity of FreiburgVerified email at informatik.uni-freiburg.de
Richard DeardenReader in Intelligent Robotics, University of BirminghamVerified email at cs.bham.ac.uk
Michael TangermannBrain State Decoding Lab and Autonomous Intelligent Systems Lab, University of Freiburg, GermanyVerified email at blbt.uni-freiburg.de
Kevin MurphyResearch Scientist, GoogleVerified email at google.com
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

Machine Learning Deep Learning Artificial Intelligence Automated Algorithm Design Combinatorial Optimization


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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	1337	2011
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	860	2013
ParamILS: an automatic algorithm configuration framework F Hutter, HH Hoos, K Leyton-Brown, T Stützle Journal of Artificial Intelligence Research 36, 267-306, 2009	816	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	770	2008
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter arXiv preprint arXiv:1608.03983, 2016	769	2016
Efficient and robust automated machine learning M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter Advances in neural information processing systems, 2962-2970, 2015	702	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	534	2017
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014	313	2014
Automatic algorithm configuration based on local search F Hutter, HH Hoos, T Stützle Aaai 7, 1152-1157, 2007	299	2007
Neural architecture search: A survey T Elsken, JH Metzen, F Hutter arXiv preprint arXiv:1808.05377, 2018	297	2018
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown The Journal of Machine Learning Research 18 (1), 826-830, 2017	294	2017
Decoupled weight decay regularization I Loshchilov, F Hutter arXiv preprint arXiv:1711.05101, 2017	284*	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	265	2015
Initializing Bayesian Hyperparameter Optimization via Meta-Learning. M Feurer, JT Springenberg, F Hutter AAAI, 1128-1135, 2015	243*	2015
Scaling and probabilistic smoothing: Efficient dynamic local search for SAT F Hutter, D Tompkins, H Hoos Principles and Practice of Constraint Programming-CP 2002, 241-249, 2002	241	2002
Bayesian optimization in high dimensions via random embeddings Z Wang, M Zoghi, F Hutter, D Matheson, N De Freitas Twenty-Third International Joint Conference on Artificial Intelligence, 2013	201	2013
Fast bayesian optimization of machine learning hyperparameters on large datasets A Klein, S Falkner, S Bartels, P Hennig, F Hutter arXiv preprint arXiv:1605.07079, 2016	191	2016
Towards an empirical foundation for assessing bayesian optimization of hyperparameters K Eggensperger, M Feurer, F Hutter, J Bergstra, J Snoek, H Hoos, ... NIPS workshop on Bayesian Optimization in Theory and Practice 10, 3, 2013	190	2013
Performance prediction and automated tuning of randomized and parametric algorithms F Hutter, Y Hamadi, HH Hoos, K Leyton-Brown International Conference on Principles and Practice of Constraint …, 2006	184	2006
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	170	2016

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