Jan N. van Rijn

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Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Luís TorgoCanada Research Chair, Professor, Faculty of Computer Science, Dalhousie UniversityVerified email at dal.ca
Felix MohrUniversidad de La Sabana - ColombiaVerified email at unisabana.edu.co
Aske PlaatLeiden UniversityVerified email at liacs.leidenuniv.nl
Bernhard PfahringerProfessor of Computer Science, University of WaikatoVerified email at waikato.ac.nz
Geoffrey HolmesUniversity of WaikatoVerified email at waikato.ac.nz
Andreas Christian MuellerMicrosoftVerified email at microsoft.com
Matthias FeurerLMU Munich & Munich Center for Machine LearningVerified email at stat.uni-muenchen.de
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchVerified email at ai.uni-hannover.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningVerified email at campus.lmu.de
Florian PfistererLudwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Peter van der PuttenAssistant Professor, LIACS, Leiden UniversityVerified email at liacs.leidenuniv.nl
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaVerified email at utfpr.edu.br
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
Frank W. TakesAssociate Professor, Leiden UniversityVerified email at liacs.nl
Walter KostersAssociate professor, Universiteit LeidenVerified email at liacs.leidenuniv.nl

Jan N. van Rijn

Leiden University

Verified email at liacs.leidenuniv.nl - Homepage

Machine Learning Artificial Intelligence Trustworthy AI


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OpenML: networked science in machine learning J Vanschoren, JN Van Rijn, B Bischl, L Torgo ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014	1213	2014
A survey of deep meta-learning M Huisman, JN Van Rijn, A Plaat Artificial Intelligence Review 54 (6), 4483-4541, 2021	239	2021
Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	228	2018
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... arXiv:1708.03731, 2017	127*	2017
The online performance estimation framework: heterogeneous ensemble learning for data streams JN van Rijn, G Holmes, B Pfahringer, J Vanschoren Machine Learning 107, 149-176, 2018	108	2018
Fast algorithm selection using learning curves JN van Rijn, SM Abdulrahman, P Brazdil, J Vanschoren Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015	95	2015
OpenML: A collaborative science platform JN Van Rijn, B Bischl, L Torgo, B Gao, V Umaashankar, S Fischer, ... Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013	87	2013
Algorithm selection on data streams JN van Rijn, G Holmes, B Pfahringer, J Vanschoren Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia …, 2014	71	2014
Speeding up algorithm selection using average ranking and active testing by introducing runtime SM Abdulrahman, P Brazdil, JN van Rijn, J Vanschoren Machine learning 107, 79-108, 2018	67	2018
Openml-python: an extensible python api for openml M Feurer, JN Van Rijn, A Kadra, P Gijsbers, N Mallik, S Ravi, A Müller, ... The Journal of Machine Learning Research 22 (1), 4573-4577, 2021	64	2021
Having a blast: Meta-learning and heterogeneous ensembles for data streams JN van Rijn, G Holmes, B Pfahringer, J Vanschoren 2015 ieee international conference on data mining, 1003-1008, 2015	59	2015
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	53*	2019
Does feature selection improve classification? a large scale experiment in OpenML MJ Post, P Van Der Putten, JN Van Rijn Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA …, 2016	37	2016
Learning Curves for Decision Making in Supervised Machine Learning--A Survey F Mohr, JN van Rijn arXiv preprint arXiv:2201.12150, 2022	35	2022
Metalearning: applications to automated machine learning and data mining P Brazdil, JN van Rijn, C Soares, J Vanschoren Springer Nature, 2022	33	2022
Learning multiple defaults for machine learning algorithms F Pfisterer, JN van Rijn, P Probst, AC Müller, B Bischl Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021	32	2021
Don’t rule out simple models prematurely: A large scale benchmark comparing linear and non-linear classifiers in OpenML B Strang, P Putten, JN Rijn, F Hutter Advances in Intelligent Data Analysis XVII: 17th International Symposium …, 2018	24	2018
Open algorithm selection challenge 2017: Setup and scenarios M Lindauer, JN van Rijn, L Kotthoff Open Algorithm Selection Challenge 2017, 1-7, 2017	23	2017
Massively collaborative machine learning JN van Rijn Leiden University, 2016	22	2016
Hyperparameter importance for image classification by residual neural networks A Sharma, JN van Rijn, F Hutter, A Müller Discovery Science: 22nd International Conference, DS 2019, Split, Croatia …, 2019	19	2019

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