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
Mike HuismanLeiden 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
Aske PlaatLeiden UniversityVerified email at liacs.leidenuniv.nl
Felix MohrUniversidad de La Sabana - ColombiaVerified email at unisabana.edu.co
Andreas Christian MuellerMicrosoftVerified email at microsoft.com
Matthias FeurerMachine Learning group, University of FreiburgVerified email at informatik.uni-freiburg.de
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence, L3S Research CenterVerified email at ai.uni-hannover.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Michel LangTU Dortmund; LMU MunichVerified email at statistik.tu-dortmund.de
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
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	1078	2014
Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	196	2018
A survey of deep meta-learning M Huisman, JN Van Rijn, A Plaat Artificial Intelligence Review 54 (6), 4483-4541, 2021	164	2021
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... arXiv:1708.03731, 2017	106*	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	99	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	87	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	80	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	70	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	62	2018
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	55	2015
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	50	2021
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	49*	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	31	2016
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	28	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	21	2018
Open algorithm selection challenge 2017: Setup and scenarios M Lindauer, JN van Rijn, L Kotthoff Open Algorithm Selection Challenge 2017, 1-7, 2017	21	2017
Massively collaborative machine learning JN van Rijn Leiden University, 2016	21	2016
Algorithm selection via meta-learning and sample-based active testing SM Abdulrhaman, P Brazdil, JN Van Rijn, J Vanschoren	20	2015
Metalearning: applications to automated machine learning and data mining P Brazdil, JN van Rijn, C Soares, J Vanschoren Springer Nature, 2022	19	2022
Learning Curves for Decision Making in Supervised Machine Learning--A Survey F Mohr, JN van Rijn arXiv preprint arXiv:2201.12150, 2022	17	2022

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