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
Felix MohrUniversidad de La Sabana - ColombiaVerified email at unisabana.edu.co
Frank HutterELLIS Institute Tübingen & Professor, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Aske PlaatLeiden UniversityVerified email at liacs.leidenuniv.nl
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Bernhard PfahringerProfessor of Computer Science, University of WaikatoVerified email at waikato.ac.nz
Geoffrey HolmesUniversity of WaikatoVerified email at waikato.ac.nz
Matthias FeurerLMU MunichVerified email at stat.uni-muenchen.de
Andreas Christian MuellerMicrosoftVerified email at microsoft.com
Matthias KönigLeiden University, RWTH Aachen University, University of OxfordVerified email at cs.ox.ac.uk
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
Thomas BäckProfessor of Computer Science, Leiden University; Chief Scientist, NORCE Research Centre, NorwayVerified email at liacs.leidenuniv.nl
Frank W. TakesAssociate Professor, Leiden UniversityVerified email at liacs.nl
Annelot BosmanPhD candidate AutoML, Universiteit LeidenVerified 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
Inês GomesPhD Student @ Porto University (FEUP)Verified email at fe.up.pt
Pavel BrazdilProfessor Emeritus, FEP, U.Porto, Member of LIAAD-INESC TEC

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	1545	2014
A survey of deep meta-learning M Huisman, JN Van Rijn, A Plaat Artificial Intelligence Review 54 (6), 4483-4541, 2021	392	2021
Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018	294	2018
Openml benchmarking suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... Proceedings of the Neural Information Processing Systems Track on Datasets …, 2021	145*	2021
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	124	2018
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	106	2013
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	104	2021
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	104	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, 79-108, 2018	82	2018
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050, 11, 2017	82	2017
Algorithm selection on data streams JN van Rijn, G Holmes, B Pfahringer, J Vanschoren Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia …, 2014	76	2014
Metalearning: applications to automated machine learning and data mining P Brazdil, JN Van Rijn, C Soares, J Vanschoren Springer Nature, 2022	71	2022
Learning Curves for Decision Making in Supervised Machine Learning--A Survey F Mohr, JN van Rijn arXiv preprint arXiv:2201.12150, 2022	68	2022
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	68*	2019
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	64	2015
Artificial intelligence to advance Earth observation: a perspective D Tuia, K Schindler, B Demir, G Camps-Valls, XX Zhu, M Kochupillai, ... arXiv preprint arXiv:2305.08413, 2023	45	2023
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	44	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	39	2021
Meta-album: Multi-domain meta-dataset for few-shot image classification I Ullah, D Carrión-Ojeda, S Escalera, I Guyon, M Huisman, F Mohr, ... Advances in Neural Information Processing Systems 35, 3232-3247, 2022	37	2022
Fast and informative model selection using learning curve cross-validation F Mohr, JN van Rijn IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (8), 9669-9680, 2023	35	2023

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