Lars Kotthoff

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Holger HoosLeiden University, The Netherlands & University of British Columbia, CanadaVerified email at liacs.nl
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Ian GentProfessor of Computer Science, St Andrews UniversityVerified email at st-andrews.ac.uk
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Ian MiguelUniversity of St AndrewsVerified email at st-andrews.ac.uk
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Peter NightingaleUniversity of YorkVerified email at york.ac.uk
Barry O'SullivanSchool of Computer Science & IT, University College Cork, IrelandVerified email at cs.ucc.ie
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Christopher JeffersonUniversity of St AndrewsVerified email at st-andrews.ac.uk
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningVerified email at campus.lmu.de
Yuri MalitskyIBM ResearchVerified email at us.ibm.com
Zachary M. JonesUniversity of WashingtonVerified email at uw.edu
Marius LindauerLeibniz University Hannover (Germany)Verified email at tnt.uni-hannover.de
Barry HurleyUniversity College CorkVerified email at 4c.ucc.ie
Pascal KerschkeProfessor of Big Data Analytics in Transportation, TU Dresden, GermanyVerified email at tu-dresden.de
Alexandre FréchettePhD Student, University of British ColumbiaVerified email at cs.ubc.ca
Tom KelseyProfessor of Health Data Science, University of St AndrewsVerified email at st-andrews.ac.uk

Lars Kotthoff

University of Wyoming

Verified email at uwyo.edu - Homepage

combinatorial optimization applied machine learning algorithm selection automated machine learning advanced materials


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Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	595	2019
mlr: Machine Learning in R B Bischl, M Lang, L Kotthoff, J Schiffner, J Richter, E Studerus, ... The Journal of Machine Learning Research 17 (1), 5938-5942, 2016	592	2016
Auto-WEKA: Automatic model selection and hyperparameter optimization in WEKA L Kotthoff, C Thornton, HH Hoos, F Hutter, K Leyton-Brown Automated Machine Learning, 81-95, 2019	563	2019
Algorithm selection for combinatorial search problems: A survey L Kotthoff Data Mining and Constraint Programming, 149-190, 2016	336	2016
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	174	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	122	2012
Fselector: selecting attributes P Romanski, L Kotthoff Vienna: R Foundation for Statistical Computing, 2009	78	2009
Proteus: A hierarchical portfolio of solvers and transformations B Hurley, L Kotthoff, Y Malitsky, B O’Sullivan International Conference on Integration of Constraint Programming …, 2014	60	2014
Package ‘FSelector’ P Romanski, L Kotthoff, ML Kotthoff URL http://cran/r-project. org/web/packages/FSelector/index. html, 2013	53*	2013
Improving the state of the art in inexact TSP solving using per-instance algorithm selection L Kotthoff, P Kerschke, H Hoos, H Trautmann International Conference on Learning and Intelligent Optimization, 202-217, 2015	51	2015
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	47	2019
LLAMA: leveraging learning to automatically manage algorithms L Kotthoff arXiv preprint arXiv:1306.1031, 2013	44	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	39	2010
Leveraging TSP solver complementarity through machine learning P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann Evolutionary computation 26 (4), 597-620, 2018	36	2018
Portfolios of subgraph isomorphism algorithms L Kotthoff, C McCreesh, C Solnon International Conference on Learning and Intelligent Optimization, 107-122, 2016	35	2016
Automated symmetry breaking and model selection in Conjure O Akgun, AM Frisch, IP Gent, BS Hussain, C Jefferson, L Kotthoff, I Miguel, ... International Conference on Principles and Practice of Constraint …, 2013	32	2013
The Algorithm Selection Competition Series 2015-17 M Lindauer, JN van Rijn, L Kotthoff arXiv preprint arXiv:1805.01214, 2018	30*	2018
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	28	2016
The ICON challenge on algorithm selection L Kotthoff, B Hurley, B O'Sullivan AI Magazine 38 (2), 91-93, 2017	21	2017
The semigroups of order 10 A Distler, C Jefferson, T Kelsey, L Kotthoff International Conference on Principles and Practice of Constraint …, 2012	19	2012

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