Frank Hutter
Frank Hutter
Professor of Computer Science, University of Freiburg, Germany
Verified email at - Homepage
TitleCited byYear
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
ParamILS: an automatic algorithm configuration framework
F Hutter, HH Hoos, K Leyton-Brown, T Stützle
Journal of Artificial Intelligence Research 36, 267-306, 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
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
Sgdr: Stochastic gradient descent with warm restarts
I Loshchilov, F Hutter
arXiv preprint arXiv:1608.03983, 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
Automatic algorithm configuration based on local search
F Hutter, HH Hoos, T Stützle
Aaai 7, 1152-1157, 2007
Algorithm runtime prediction: Methods & evaluation
F Hutter, L Xu, HH Hoos, K Leyton-Brown
Artificial Intelligence 206, 79-111, 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
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
Initializing Bayesian Hyperparameter Optimization via Meta-Learning.
M Feurer, JT Springenberg, F Hutter
AAAI, 1128-1135, 2015
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
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
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
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
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
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
Automated configuration of mixed integer programming solvers
F Hutter, HH Hoos, K Leyton-Brown
International Conference on Integration of Artificial Intelligence (AI) and …, 2010
SATzilla-07: The Design and Analysis of an Algorithm Portfolio for SAT
L Xu, F Hutter, H Hoos, K Leyton-Brown
Principles and Practice of Constraint Programming–CP 2007, 712-727, 2007
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