Frank Hutter
Frank Hutter
Professor of Computer Science, University of Freiburg, Germany
Verified email at - Homepage
Cited by
Cited by
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
Sgdr: Stochastic gradient descent with warm restarts
I Loshchilov, F Hutter
arXiv preprint arXiv:1608.03983, 2016
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
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
ParamILS: an automatic algorithm configuration framework
F Hutter, HH Hoos, K Leyton-Brown, T Stützle
Journal of Artificial Intelligence Research 36, 267-306, 2009
Decoupled weight decay regularization
I Loshchilov, F Hutter
arXiv preprint arXiv:1711.05101, 2017
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
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
Neural architecture search: A survey.
T Elsken, JH Metzen, F Hutter
J. Mach. Learn. Res. 20 (55), 1-21, 2019
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
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
Algorithm runtime prediction: Methods & evaluation
F Hutter, L Xu, HH Hoos, K Leyton-Brown
Artificial Intelligence 206, 79-111, 2014
Automatic algorithm configuration based on local search
F Hutter, HH Hoos, T Stützle
Aaai 7, 1152-1157, 2007
Automated machine learning: methods, systems, challenges
F Hutter, L Kotthoff, J Vanschoren
Springer Nature, 2019
Initializing Bayesian Hyperparameter Optimization via Meta-Learning.
M Feurer, JT Springenberg, F Hutter
AAAI, 1128-1135, 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
BOHB: Robust and efficient hyperparameter optimization at scale
S Falkner, A Klein, F Hutter
arXiv preprint arXiv:1807.01774, 2018
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
Bayesian Optimization in High Dimensions via Random Embeddings.
Z Wang, M Zoghi, F Hutter, D Matheson, N De Freitas
IJCAI, 1778-1784, 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
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