Lars Kotthoff

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Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaAdresse e-mail validée de cs.rwth-aachen.de
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichAdresse e-mail validée de stat.uni-muenchen.de
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyAdresse e-mail validée de cs.uni-freiburg.de
Joaquin VanschorenEindhoven University of TechnologyAdresse e-mail validée de tue.nl
Ian GentProfessor of Computer Science, St Andrews UniversityAdresse e-mail validée de st-andrews.ac.uk
Ian MiguelUniversity of St AndrewsAdresse e-mail validée de st-andrews.ac.uk
Michel LangTU Dortmund; LMU MunichAdresse e-mail validée de statistik.tu-dortmund.de
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairAdresse e-mail validée de cs.ubc.ca
Peter NightingaleUniversity of YorkAdresse e-mail validée de york.ac.uk
Barry O'SullivanSchool of Computer Science & IT, University College Cork, IrelandAdresse e-mail validée de cs.ucc.ie
Jakob RichterTU DortmundAdresse e-mail validée de statistik.tu-dortmund.de
Christopher JeffersonUniversity of St AndrewsAdresse e-mail validée de st-andrews.ac.uk
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningAdresse e-mail validée de campus.lmu.de
Yuri MalitskyIBM ResearchAdresse e-mail validée de us.ibm.com
Zachary M. JonesMetaAdresse e-mail validée de fb.com
Marius LindauerLeibniz University Hannover (Germany)Adresse e-mail validée de tnt.uni-hannover.de
Pascal KerschkeProfessor of Big Data Analytics in Transportation, TU Dresden, GermanyAdresse e-mail validée de tu-dresden.de
Barry HurleyUniversity College CorkAdresse e-mail validée de 4c.ucc.ie
Alexandre FréchettePhD Student, University of British ColumbiaAdresse e-mail validée de cs.ubc.ca
Tom KelseyProfessor of Health Data Science, University of St AndrewsAdresse e-mail validée de st-andrews.ac.uk

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Lars Kotthoff

University of Wyoming

Adresse e-mail validée de uwyo.edu - Page d'accueil

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


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	942	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	710	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	689	2019
Algorithm selection for combinatorial search problems: A survey L Kotthoff Data mining and constraint programming, 149-190, 2016	374	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	200	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	133	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	101	2019
Fselector: selecting attributes P Romanski, L Kotthoff Vienna: R Foundation for Statistical Computing, 2009	89	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	69	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	61	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	52	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	34	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	28*	2020
The ICON challenge on algorithm selection L Kotthoff, B Hurley, B O'Sullivan AI Magazine 38 (2), 91-93, 2017	25	2017

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