Frank Hutter - Google Scholar Citations

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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
Lin XuUniversity of British ColumbiaVerified email at cs.ubc.ca
Marius LindauerUniversity of Freiburg/HannoverVerified email at tnt.uni-hannover.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
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Tonio BallHead of Neuromedical AI Lab FreiburgVerified email at uniklinik-freiburg.de
Thomas StützleF.R.S.-FNRS Research Director, Université Libre de Bruxelles (ULB)Verified email at ulb.ac.be
Richard DeardenReader in Intelligent Robotics, University of BirminghamVerified email at cs.bham.ac.uk
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Manuel BlumUniversity of FreiburgVerified email at informatik.uni-freiburg.de
Wolfram BurgardProfessor of Computer Science, University of FreiburgVerified email at informatik.uni-freiburg.de
Kevin MurphyResearch Scientist, GoogleVerified email at google.com
Nando de FreitasCIFAR & DeepMindVerified email at google.com
Michael TangermannBrain State Decoding Lab and Autonomous Intelligent Systems Lab, University of Freiburg, GermanyVerified email at blbt.uni-freiburg.de

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


Title	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	1127	2011
ParamILS: an automatic algorithm configuration framework F Hutter, HH Hoos, K Leyton-Brown, T Stützle Journal of Artificial Intelligence Research 36, 267-306, 2009	763	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	735	2008
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	706	2013
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	492	2015
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter arXiv preprint arXiv:1608.03983, 2016	484	2016
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	351*	2017
Automatic algorithm configuration based on local search F Hutter, HH Hoos, T Stützle Aaai 7, 1152-1157, 2007	287	2007
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014	282	2014
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	232	2002
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	212	2015
Initializing Bayesian Hyperparameter Optimization via Meta-Learning. M Feurer, JT Springenberg, F Hutter AAAI, 1128-1135, 2015	202*	2015
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	185	2017
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	183	2006
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	171	2013
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	159	2013
A new algorithm for RNA secondary structure design M Andronescu, AP Fejes, F Hutter, HH Hoos, A Condon Journal of molecular biology 336 (3), 607-624, 2004	157	2004
Boosting verification by automatic tuning of decision procedures F Hutter, D Babic, HH Hoos, AJ Hu Formal Methods in Computer Aided Design (FMCAD'07), 27-34, 2007	144	2007
Automated configuration of mixed integer programming solvers F Hutter, HH Hoos, K Leyton-Brown International Conference on Integration of Artificial Intelligence (AI) and …, 2010	143	2010
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	134	2016

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