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
Matthias FeurerMachine Learning group, University of FreiburgVerified email at informatik.uni-freiburg.de
Andreas Christian MuellerMicrosoftVerified email at microsoft.com
Aske PlaatLeidenVerified email at liacs.leidenuniv.nl
Mike HuismanLeiden UniversityVerified email at liacs.leidenuniv.nl
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)Verified 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
Ross D. KingProfessor of Machine Intelligence, Chalmers UniversityVerified email at chalmers.se

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	888	2014
Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	149	2018
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	82	2018
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... arXiv:1708.03731, 2017	80*	2017
Fast algorithm selection using learning curves JN Rijn, SM Abdulrahman, P Brazdil, J Vanschoren International symposium on intelligent data analysis, 298-309, 2015	80	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	77	2013
A survey of deep meta-learning M Huisman, JN Van Rijn, A Plaat Artificial Intelligence Review 54 (6), 4483-4541, 2021	71	2021
Algorithm selection on data streams JN Rijn, G Holmes, B Pfahringer, J Vanschoren International Conference on Discovery Science, 325-336, 2014	70	2014
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	52	2015
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	47	2018
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	38	2019
Openml-python: an extensible python api for openml M Feurer, JN van Rijn, A Kadra, P Gijsbers, N Mallik, S Ravi, A Müller, ... arXiv preprint arXiv:1911.02490, 2019	37	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
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	18	2018
Open algorithm selection challenge 2017: Setup and scenarios M Lindauer, JN van Rijn, L Kotthoff Open Algorithm Selection Challenge 2017, 1-7, 2017	18	2017
Algorithm selection via meta-learning and sample-based active testing SM Abdulrhaman, P Brazdil, JN Van Rijn, J Vanschoren	18	2015
An Empirical Study of Hyperparameter Importance Across Datasets. JN Van Rijn, F Hutter AutoML@ PKDD/ECML, 91-98, 2017	13	2017
Playing Games: The complexity of Klondike, Mahjong, Nonograms and Animal Chess JN van Rijn	12	2012

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