| Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019 | 2168 | 2019 |
| 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 (25), 1-5, 2017 | 995 | 2017 |
| mlr: Machine Learning in R B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, E Studerus, ... Journal of Machine Learning Research 17 (170), 1-5, 2016 | 950 | 2016 |
| Algorithm selection for combinatorial search problems: A survey L Kotthoff Data mining and constraint programming: Foundations of a cross-disciplinary …, 2016 | 481 | 2016 |
| mlr3: A modern object-oriented machine learning framework in R M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ... Journal of Open Source Software 4 (44), 1903, 2019 | 329 | 2019 |
| Aslib: A benchmark library for algorithm selection B Bischl, P Kerschke, L Kotthoff, M Lindauer, Y Malitsky, A Fréchette, ... Artificial Intelligence 237, 41-58, 2016 | 289 | 2016 |
| An evaluation of machine learning in algorithm selection for search problems L Kotthoff, IP Gent, I Miguel Ai Communications 25 (3), 257-270, 2012 | 105 | 2012 |
| Fselector: selecting attributes P Romanski, L Kotthoff Vienna: R Foundation for Statistical Computing, 2009 | 99 | 2009 |
| Leveraging TSP solver complementarity through machine learning P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann Evolutionary computation 26 (4), 597-620, 2018 | 93 | 2018 |
| Proteus: A hierarchical portfolio of solvers and transformations B Hurley, L Kotthoff, Y Malitsky, B O’Sullivan Integration of AI and OR Techniques in Constraint Programming: 11th …, 2014 | 84 | 2014 |
| Machine-learning-assisted fabrication: Bayesian optimization of laser-induced graphene patterning using in-situ Raman analysis H Wahab, V Jain, AS Tyrrell, MA Seas, L Kotthoff, PA Johnson Carbon 167, 609-619, 2020 | 78 | 2020 |
| Improving the state of the art in inexact TSP solving using per-instance algorithm selection L Kotthoff, P Kerschke, H Hoos, H Trautmann Learning and Intelligent Optimization: 9th International Conference, LION 9 …, 2015 | 77 | 2015 |
| The Algorithm Selection Competition Series 2015-17 M Lindauer, JN van Rijn, L Kotthoff arXiv preprint arXiv:1805.01214, 2018 | 67* | 2018 |
| Package ‘FSelector’ P Romanski, L Kotthoff, ML Kotthoff URL http://cran/r-project. org/web/packages/FSelector/index. html, 2013 | 57 | 2013 |
| Learning when to use lazy learning in constraint solving IP Gent, C Jefferson, L Kotthoff, I Miguel, NCA Moore, P Nightingale, ... ECAI 2010, 873-878, 2010 | 55 | 2010 |
| Portfolios of subgraph isomorphism algorithms L Kotthoff, C McCreesh, C Solnon International Conference on Learning and Intelligent Optimization, 107-122, 2016 | 54 | 2016 |
| Using the shapley value to analyze algorithm portfolios A Fréchette, L Kotthoff, T Michalak, T Rahwan, H Hoos, K Leyton-Brown Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 50 | 2016 |
| LLAMA: leveraging learning to automatically manage algorithms L Kotthoff arXiv preprint arXiv:1306.1031, 2013 | 50 | 2013 |
| A preliminary evaluation of machine learning in algorithm selection for search problems L Kotthoff, I Gent, I Miguel Proceedings of the International Symposium on Combinatorial Search 2 (1), 84-91, 2011 | 41 | 2011 |
| Automated Symmetry Breaking and Model Selection in Conjure O Akgun, AM Frisch, IP Gent, BS Hussain, C Jefferson, L Kotthoff, I Miguel, ... Principles and Practice of Constraint Programming: 19th International …, 2013 | 40 | 2013 |
Frank HutterELLIS Institute & Professor, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
