| MAX–MIN ant system T Stützle, HH Hoos Future generation computer systems 16 (8), 889-914, 2000 | 4618 | 2000 |
| 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 | 3826 | 2011 |
| A survey on semi-supervised learning JE Van Engelen, HH Hoos Machine learning 109 (2), 373-440, 2020 | 3676 | 2020 |
| Stochastic local search HH Hoos, T Stϋtzle Handbook of approximation algorithms and metaheuristics, 297-307, 2018 | 2649 | 2018 |
| 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 | 2328 | 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 | 1491 | 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 | 1420 | 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 | 1308 | 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 | 1289 | 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 | 1102 | 2017 |
| An efficient approach for assessing hyperparameter importance F Hutter, H Hoos, K Leyton-Brown | 795 | 2014 |
| Critical assessment of automated flow cytometry data analysis techniques N Aghaeepour, G Finak, FlowCAP Consortium, Dream Consortium, ... Nature methods 10 (3), 228-238, 2013 | 721 | 2013 |
| Algorithm runtime prediction: Methods & evaluation F Hutter, L Xu, HH Hoos, K Leyton-Brown Artificial Intelligence 206, 79-111, 2014 | 660 | 2014 |
| Automated algorithm selection: Survey and perspectives P Kerschke, HH Hoos, F Neumann, H Trautmann Evolutionary computation 27 (1), 3-45, 2019 | 637 | 2019 |
| A research agenda for hybrid intelligence: augmenting human intellect with collaborative, adaptive, responsible, and explainable artificial intelligence Z Akata, D Balliet, M De Rijke, F Dignum, V Dignum, G Eiben, A Fokkens, ... Computer 53 (8), 18-28, 2020 | 586 | 2020 |
| SATLIB: An online resource for research on SAT HH Hoos, T Stützle Sat 2000, 283-292, 2000 | 559 | 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 | 528 | 2013 |
| Improvements on the Ant-System: Introducing the MAX-MIN Ant System T Stützle, H Hoos Artificial Neural Nets and Genetic Algorithms: Proceedings of the …, 1998 | 509 | 1998 |
| Automatic algorithm configuration based on local search F Hutter, HH Hoos, T Stützle Aaai 7, 1152-1157, 2007 | 434 | 2007 |
| An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem A Shmygelska, HH Hoos BMC bioinformatics 6 (1), 30, 2005 | 396 | 2005 |
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
