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
Pavel BrazdilProfessor at FEP, U.Porto, Member of LIAAD-INESC TecVerified email at inescporto.pt
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)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
Aske PlaatLeidenVerified email at liacs.leidenuniv.nl
Mike HuismanLeiden UniversityVerified email at liacs.leidenuniv.nl
Peter van der PuttenAssistant Professor, LIACS, Leiden UniversityVerified email at liacs.leidenuniv.nl
Florian PfistererPhD Candidate, Ludwig Maximilian University of MunichVerified email at stat.uni-muenchen.de
Frank W. TakesLeiden University, University of AmsterdamVerified 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


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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	729	2014
Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	90	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	70	2018
OpenML: A collaborative science platform JN Van Rijn, B Bischl, L Torgo, B Gao, V Umaashankar, S Fischer, ... Joint european conference on machine learning and knowledge discovery in …, 2013	68	2013
Fast algorithm selection using learning curves JN van Rijn, SM Abdulrahman, P Brazdil, J Vanschoren International symposium on intelligent data analysis, 298-309, 2015	67	2015
Algorithm selection on data streams JN van Rijn, G Holmes, B Pfahringer, J Vanschoren International Conference on Discovery Science, 325-336, 2014	62	2014
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... arXiv:1708.03731, 2017	61*	2017
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	39	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	33	2018
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	30*	2019
Openml-python: an extensible python api for openml M Feurer, JN van Rijn, A Kadra, P Gijsbers, N Mallik, S Ravi, A Müller, ... Journal of Machine Learning Research 22 (100), 1-5, 2021	23	2021
Does feature selection improve classification? a large scale experiment in OpenML MJ Post, P Van Der Putten, JN Van Rijn International Symposium on Intelligent Data Analysis, 158-170, 2016	23	2016
A survey of deep meta-learning M Huisman, JN van Rijn, A Plaat Artificial Intelligence Review, 1-59, 2021	19	2021
Algorithm selection via meta-learning and sample-based active testing SM Abdulrhaman, P Brazdil, JN Van Rijn, J Vanschoren	18	2015
Don’t rule out simple models prematurely: a large scale benchmark comparing linear and non-linear classifiers in OpenML B Strang, P van der Putten, JN van Rijn, F Hutter International Symposium on Intelligent Data Analysis, 303-315, 2018	13	2018
Massively collaborative machine learning JN van Rijn Leiden University, 2016	13	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	12	2021
Open algorithm selection challenge 2017: Setup and scenarios M Lindauer, JN van Rijn, L Kotthoff Open Algorithm Selection Challenge 2017, 1-7, 2017	12	2017
An Empirical Study of Hyperparameter Importance Across Datasets. JN Van Rijn, F Hutter AutoML@ PKDD/ECML, 91-98, 2017	12	2017
Playing Games: The complexity of Klondike, Mahjong, Nonograms and Animal Chess JN van Rijn	12	2012

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