Frank Hutter
Frank Hutter
Professor of Computer Science, University of Freiburg, Germany
Verified email at cs.uni-freiburg.de - Homepage
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
19152011
Sgdr: Stochastic gradient descent with warm restarts
I Loshchilov, F Hutter
arXiv preprint arXiv:1608.03983, 2016
18732016
Decoupled weight decay regularization
I Loshchilov, F Hutter
arXiv preprint arXiv:1711.05101, 2017
1731*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
12982019
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
11742013
Neural architecture search: A survey
T Elsken, JH Metzen, F Hutter
The Journal of Machine Learning Research 20 (1), 1997-2017, 2019
10012019
ParamILS: an automatic algorithm configuration framework
F Hutter, HH Hoos, K Leyton-Brown, T Stützle
Journal of Artificial Intelligence Research 36, 267-306, 2009
9752009
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
9262017
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
8912008
Automated machine learning: methods, systems, challenges
F Hutter, L Kotthoff, J Vanschoren
Springer Nature, 2019
5302019
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
5142019
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
5142019
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
5112017
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
4312015
Algorithm runtime prediction: Methods & evaluation
F Hutter, L Xu, HH Hoos, K Leyton-Brown
Artificial Intelligence 206, 79-111, 2014
4012014
BOHB: Robust and efficient hyperparameter optimization at scale
S Falkner, A Klein, F Hutter
International Conference on Machine Learning, 1437-1446, 2018
3872018
Initializing Bayesian Hyperparameter Optimization via Meta-Learning.
M Feurer, JT Springenberg, F Hutter
AAAI, 1128-1135, 2015
351*2015
Automatic algorithm configuration based on local search
F Hutter, HH Hoos, T Stützle
Aaai 7, 1152-1157, 2007
3452007
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
3442017
Hyperparameter optimization
M Feurer, F Hutter
Automated machine learning, 3-33, 2019
3142019
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