Holger Hoos
Holger Hoos
RWTH Aachen University, Germany • Leiden University, Netherlands • University of British Columbia
Zweryfikowany adres z cs.rwth-aachen.de - Strona główna
Cytowane przez
Cytowane przez
MAX–MIN ant system
T Stützle, HH Hoos
Future generation computer systems 16 (8), 889-914, 2000
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
Stochastic local search
HH Hoos, T Stϋtzle
Handbook of Approximation Algorithms and Metaheuristics, 297-307, 2018
A survey on semi-supervised learning
JE Van Engelen, HH Hoos
Machine learning 109 (2), 373-440, 2020
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
MAX-MIN ant system and local search for the traveling salesman problem
T Stutzle, H Hoos
IEEE International Conference on Evolutionary Computation 1997, 309-314, 1997
ParamILS: an automatic algorithm configuration framework
F Hutter, HH Hoos, K Leyton-Brown, T Stützle
Journal of artificial intelligence research 36, 267-306, 2009
CP-nets: A tool for representing and reasoning withconditional ceteris paribus preference statements
C Boutilier, RI Brafman, C Domshlak, HH Hoos, D Poole
Journal of artificial intelligence research 21, 135-191, 2004
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 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, 1-5, 2017
Critical assessment of automated flow cytometry data analysis techniques
N Aghaeepour, G Finak, FlowCAP Consortium, Dream Consortium, ...
Nature methods 10 (3), 228-238, 2013
An efficient approach for assessing hyperparameter importance
F Hutter, H Hoos, K Leyton-Brown
Algorithm runtime prediction: Methods & evaluation
F Hutter, L Xu, HH Hoos, K Leyton-Brown
Artificial Intelligence 206, 79-111, 2014
Improvements on the Ant-System: Introducing the MAX-MIN Ant System
GD Smith, NC Steele, RF Albrecht, T Stützle, H Hoos
Artificial Neural Nets and Genetic Algorithms: Proceedings of the …, 1998
SATLIB: An online resource for research on SAT
HH Hoos, T Stützle
Sat 2000, 283-292, 2000
Automated algorithm selection: Survey and perspectives
P Kerschke, HH Hoos, F Neumann, H Trautmann
Evolutionary computation 27 (1), 3-45, 2019
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), 1-5, 2013
Automatic algorithm configuration based on local search
F Hutter, HH Hoos, T Stützle
Aaai 7, 1152-1157, 2007
An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem
A Shmygelska, HH Hoos
BMC bioinformatics 6, 1-22, 2005
Reasoning With Conditional Ceteris Paribus Preference Statements.
C Boutilier, RI Brafman, HH Hoos, D Poole
UAI 99, 71-80, 1999
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