MAX–MIN ant system T Stützle, HH Hoos Future generation computer systems 16 (8), 889-914, 2000 | 4472 | 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 | 3191 | 2011 |
Stochastic local search HH Hoos, T Stϋtzle Handbook of Approximation Algorithms and Metaheuristics, 297-307, 2018 | 2455 | 2018 |
A survey on semi-supervised learning JE Van Engelen, HH Hoos Machine learning 109 (2), 373-440, 2020 | 2328 | 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 | 1996 | 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 | 1452 | 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 | 1255 | 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 | 1247 | 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 | 1138 | 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 | 944 | 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 | 670 | 2013 |
An efficient approach for assessing hyperparameter importance F Hutter, H Hoos, K Leyton-Brown | 582 | 2014 |
Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014 | 560 | 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 | 507 | 1998 |
SATLIB: An online resource for research on SAT HH Hoos, T Stützle Sat 2000, 283-292, 2000 | 490 | 2000 |
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 | 457 | 2013 |
Automated algorithm selection: Survey and perspectives P Kerschke, HH Hoos, F Neumann, H Trautmann Evolutionary computation 27 (1), 3-45, 2019 | 455 | 2019 |
Automatic algorithm configuration based on local search F Hutter, HH Hoos, T Stützle Aaai 7, 1152-1157, 2007 | 401 | 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 | 380 | 2005 |
Reasoning With Conditional Ceteris Paribus Preference Statements. C Boutilier, RI Brafman, HH Hoos, D Poole UAI 99, 71-80, 1999 | 368 | 1999 |