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
Bernhard PfahringerProfessor of Computer Science, University of WaikatoVerified email at waikato.ac.nz
Geoffrey HolmesUniversity of WaikatoVerified email at waikato.ac.nz
Mike HuismanLeiden UniversityVerified email at liacs.leidenuniv.nl
Aske PlaatLeidenVerified email at liacs.leidenuniv.nl
Matthias FeurerMachine Learning group, University of FreiburgVerified email at informatik.uni-freiburg.de
Felix MohrUniversidad de La Sabana - ColombiaVerified email at unisabana.edu.co
Andreas Christian MuellerMicrosoftVerified email at microsoft.com
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
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence, L3S Research CenterVerified email at tnt.uni-hannover.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaVerified email at utfpr.edu.br
Florian PfistererPhD Candidate, Ludwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Frank W. TakesAssociate Professor, Leiden UniversityVerified email at liacs.nl
Peter van der PuttenAssistant Professor, LIACS, Leiden UniversityVerified email at liacs.leidenuniv.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	1004	2014
Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	179	2018
A survey of deep meta-learning M Huisman, JN Van Rijn, A Plaat Artificial Intelligence Review 54 (6), 4483-4541, 2021	127	2021
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... arXiv:1708.03731, 2017	96*	2017
The online performance estimation framework: heterogeneous ensemble learning for data streams JN van Rijn, G Holmes, B Pfahringer, J Vanschoren Machine Learning 107 (1), 149-176, 2018	92	2018
Fast algorithm selection using learning curves JN Rijn, SM Abdulrahman, P Brazdil, J Vanschoren International symposium on intelligent data analysis, 298-309, 2015	85	2015
OpenML: A collaborative science platform JN Rijn, B Bischl, L Torgo, B Gao, V Umaashankar, S Fischer, P Winter, ... Joint european conference on machine learning and knowledge discovery in …, 2013	79	2013
Algorithm selection on data streams JN Rijn, G Holmes, B Pfahringer, J Vanschoren International Conference on Discovery Science, 325-336, 2014	73	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 (1), 79-108, 2018	57	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	54	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	47	2021
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	44	2019
Does feature selection improve classification? a large scale experiment in OpenML MJ Post, P Putten, JN Rijn International Symposium on Intelligent Data Analysis, 158-170, 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	26	2021
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
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 International Symposium on Intelligent Data Analysis, 303-315, 2018	20	2018
Algorithm selection via meta-learning and sample-based active testing SM Abdulrhaman, P Brazdil, JN Van Rijn, J Vanschoren	19	2015
An Empirical Study of Hyperparameter Importance Across Datasets. JN Van Rijn, F Hutter AutoML@ PKDD/ECML, 91-98, 2017	14	2017
Metalearning: Applications to Automated Machine Learning and Data Mining P Brazdil, JN van Rijn, C Soares, J Vanschoren Springer Nature, 2022	12	2022

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