Frank Hutter

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Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaDirección de correo verificada de cs.rwth-aachen.de
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairDirección de correo verificada de cs.ubc.ca
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchDirección de correo verificada de ai.uni-hannover.de
Ilya LoshchilovNVIDIADirección de correo verificada de nvidia.com
Katharina EggenspergerEarly Career Group Leader "AutoML for Science" | University of TübingenDirección de correo verificada de uni-tuebingen.de
Matthias FeurerLMU MunichDirección de correo verificada de stat.uni-muenchen.de
Aaron KleinScaDS.AIDirección de correo verificada de amazon.com
Stefan FalknerBosch Corporate ResearchDirección de correo verificada de de.bosch.com
Lin XuNETINT, University of British ColumbiaDirección de correo verificada de cs.ubc.ca
Tonio BallNeuromedical AI Lab Uni Freiburg, NeuroMentum AI (CEO)Dirección de correo verificada de uniklinik-freiburg.de
Joaquin VanschorenEindhoven University of TechnologyDirección de correo verificada de tue.nl
Lars KotthoffUniversity of WyomingDirección de correo verificada de uwyo.edu
Wolfram BurgardProfessor of Computer Science, Technical University of NurembergDirección de correo verificada de utn.de
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andDirección de correo verificada de donders.ru.nl
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichDirección de correo verificada de stat.uni-muenchen.de
Thomas StützleF.R.S.-FNRS Research Director, Université Libre de Bruxelles (ULB)Dirección de correo verificada de ulb.ac.be
Kevin MurphyResearch Scientist, GoogleDirección de correo verificada de google.com
Manuel BlumUniversity of FreiburgDirección de correo verificada de informatik.uni-freiburg.de
Nando de FreitasCIFAR & DeepMindDirección de correo verificada de google.com
Philipp HennigUniversity of TübingenDirección de correo verificada de uni-tuebingen.de

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Frank Hutter

ELLIS Institute Tübingen & Professor, University of Freiburg, Germany

Dirección de correo verificada de cs.uni-freiburg.de - Página principal

AutoML Meta-Learning Deep Learning Tabular Data Neural Architecture Search


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Decoupled weight decay regularization I Loshchilov, F Hutter ICLR 2019, 2017	24624	2017
Sgdr: Stochastic gradient descent with warm restarts I Loshchilov, F Hutter ICLR 2017, 2016	9371	2016
Neural architecture search: A survey T Elsken, JH Metzen, F Hutter Journal of Machine Learning Research 20 (55), 1-21, 2019	3490	2019
Sequential model-based optimization for general algorithm configuration F Hutter, HH Hoos, K Leyton-Brown Learning and Intelligent Optimization: 5th International Conference, LION 5 …, 2011	3375	2011
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	2973	2017
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	2957	2015
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	2206	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	2112	2013
Hyperparameter optimization M Feurer, F Hutter Automated machine learning: Methods, systems, challenges, 3-33, 2019	1744	2019
BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter International conference on machine learning, 1437-1446, 2018	1347	2018
ParamILS: an automatic algorithm configuration framework F Hutter, HH Hoos, K Leyton-Brown, T Stützle Journal of artificial intelligence research 36, 267-306, 2009	1289	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	1179	2008
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	985	2017
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	871	2016
Nas-bench-101: Towards reproducible neural architecture search C Ying, A Klein, E Christiansen, E Real, K Murphy, F Hutter International conference on machine learning, 7105-7114, 2019	831	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	811	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	746	2017
A downsampled variant of imagenet as an alternative to the cifar datasets P Chrabaszcz, I Loshchilov, F Hutter arXiv preprint arXiv:1707.08819, 2017	696	2017
Efficient multi-objective neural architecture search via lamarckian evolution T Elsken, JH Metzen, F Hutter arXiv preprint arXiv:1804.09081, 2018	654	2018
An efficient approach for assessing hyperparameter importance F Hutter, H Hoos, K Leyton-Brown International conference on machine learning, 754-762, 2014	638	2014

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