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
Jakob RichterTU DortmundVerified email at statistik.tu-dortmund.de
Peter NightingaleUniversity of YorkVerified email at york.ac.uk
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
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
Florian PfistererPhD Candidate, Ludwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence, L3S Research CenterVerified email at ai.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
Patrick SchratzPhD Candidate at Friedrich Schiller University JenaVerified email at uni-jena.de

Lars Kotthoff

University of Wyoming

Verified email at uwyo.edu - Homepage

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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	1154	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	764	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: methods, systems, challenges, 81-95, 2019	757	2019
Algorithm selection for combinatorial search problems: A survey L Kotthoff Data mining and constraint programming: Foundations of a cross-disciplinary …, 2016	397	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	212	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	148	2019
An evaluation of machine learning in algorithm selection for search problems L Kotthoff, IP Gent, I Miguel Ai Communications 25 (3), 257-270, 2012	96	2012
Fselector: selecting attributes P Romanski, L Kotthoff Vienna: R Foundation for Statistical Computing, 2009	94	2009
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	73	2014
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	65	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	58	2018
The Algorithm Selection Competition Series 2015-17 M Lindauer, JN van Rijn, L Kotthoff arXiv preprint arXiv:1805.01214, 2018	48*	2018
Package ‘FSelector’ P Romanski, L Kotthoff, ML Kotthoff URL http://cran/r-project. org/web/packages/FSelector/index. html, 2013	48	2013
LLAMA: leveraging learning to automatically manage algorithms L Kotthoff arXiv preprint arXiv:1306.1031, 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	45	2010
Portfolios of subgraph isomorphism algorithms L Kotthoff, C McCreesh, C Solnon Learning and Intelligent Optimization: 10th International Conference, LION …, 2016	43	2016
A preliminary evaluation of machine learning in algorithm selection for search problems L Kotthoff, IP Gent, I Miguel Fourth Annual Symposium on Combinatorial Search, 2011	42	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	38	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	37	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	35*	2020

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