Lars Kotthoff

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Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Ian GentProfessor of Computer Science, St Andrews UniversityVerified email at st-andrews.ac.uk
Ian MiguelUniversity of St AndrewsVerified email at st-andrews.ac.uk
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
Jakob RichterTU DortmundVerified 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
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. JonesMetaVerified email at fb.com
Marius LindauerLeibniz University Hannover (Germany)Verified email at tnt.uni-hannover.de
Pascal KerschkeProfessor of Big Data Analytics in Transportation, TU Dresden, GermanyVerified email at tu-dresden.de
Barry HurleyUniversity College CorkVerified email at 4c.ucc.ie
Florian PfistererPhD Candidate, Ludwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Alexandre FréchettePhD Student, University of British ColumbiaVerified email at cs.ubc.ca

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	993	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	727	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	703	2019
Algorithm selection for combinatorial search problems: A survey L Kotthoff Data mining and constraint programming, 149-190, 2016	382	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	201	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	134	2012
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	108	2019
Fselector: selecting attributes P Romanski, L Kotthoff Vienna: R Foundation for Statistical Computing, 2009	93	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	68	2014
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	63	2015
Leveraging TSP solver complementarity through machine learning P Kerschke, L Kotthoff, J Bossek, HH Hoos, H Trautmann Evolutionary computation 26 (4), 597-620, 2018	55	2018
LLAMA: leveraging learning to automatically manage algorithms L Kotthoff arXiv preprint arXiv:1306.1031, 2013	46	2013
Package ‘FSelector’ P Romanski, L Kotthoff, ML Kotthoff URL http://cran/r-project. org/web/packages/FSelector/index. html, 2013	46	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	44	2010
The Algorithm Selection Competition Series 2015-17 M Lindauer, JN van Rijn, L Kotthoff arXiv preprint arXiv:1805.01214, 2018	43*	2018
Portfolios of subgraph isomorphism algorithms L Kotthoff, C McCreesh, C Solnon International Conference on Learning and Intelligent Optimization, 107-122, 2016	39	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	36	2013
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	35	2016
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	29*	2020
The ICON challenge on algorithm selection L Kotthoff, B Hurley, B O'Sullivan AI Magazine 38 (2), 91-93, 2017	26	2017

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