Jan N. van Rijn

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Referințe bibliografice	3852	3350
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i10-index	38	34

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Joaquin VanschorenEindhoven University of TechnologyAdresă de e-mail confirmată pe tue.nl
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichAdresă de e-mail confirmată pe stat.uni-muenchen.de
Frank HutterELLIS Institute & Professor, University of Freiburg, GermanyAdresă de e-mail confirmată pe cs.uni-freiburg.de
Felix MohrUniversidad de La Sabana - ColombiaAdresă de e-mail confirmată pe unisabana.edu.co
Aske PlaatLeiden UniversityAdresă de e-mail confirmată pe liacs.leidenuniv.nl
Luís TorgoCanada Research Chair, Professor, Faculty of Computer Science, Dalhousie UniversityAdresă de e-mail confirmată pe dal.ca
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaAdresă de e-mail confirmată pe cs.rwth-aachen.de
Bernhard PfahringerProfessor of Computer Science, University of WaikatoAdresă de e-mail confirmată pe waikato.ac.nz
Geoffrey HolmesUniversity of WaikatoAdresă de e-mail confirmată pe waikato.ac.nz
Andreas Christian MuellerMicrosoftAdresă de e-mail confirmată pe microsoft.com
Giuseppe CasalicchioPostdoctoral Researcher, LMU Munich, Munich Center for Machine LearningAdresă de e-mail confirmată pe campus.lmu.de
Matthias FeurerLMU MunichAdresă de e-mail confirmată pe stat.uni-muenchen.de
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaAdresă de e-mail confirmată pe utfpr.edu.br
Michel LangTU Dortmund; LMU MunichAdresă de e-mail confirmată pe statistik.tu-dortmund.de
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchAdresă de e-mail confirmată pe ai.uni-hannover.de
Lars KotthoffUniversity of WyomingAdresă de e-mail confirmată pe uwyo.edu
Florian PfistererLudwig Maximilian University of MunichAdresă de e-mail confirmată pe stat.uni-muenchen.de
Peter van der PuttenAssistant Professor, LIACS, Leiden UniversityAdresă de e-mail confirmată pe liacs.leidenuniv.nl
Frank W. TakesAssociate Professor, Leiden UniversityAdresă de e-mail confirmată pe liacs.nl
Walter KostersAssociate professor, Universiteit LeidenAdresă de e-mail confirmată pe liacs.leidenuniv.nl

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Jan N. van Rijn

Leiden University

Adresă de e-mail confirmată pe liacs.leidenuniv.nl - Pagina de pornire

Machine Learning Artificial Intelligence Trustworthy AI


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
OpenML: networked science in machine learning J Vanschoren, JN Van Rijn, B Bischl, L Torgo ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014	1489	2014
A survey of deep meta-learning M Huisman, JN Van Rijn, A Plaat Artificial Intelligence Review 54 (6), 4483-4541, 2021	361	2021
Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018	283	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	125*	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	105	2013
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	103	2015
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	92	2021
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	81	2018
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050, 11, 2017	79	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	75	2014
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	66*	2019
Learning Curves for Decision Making in Supervised Machine Learning--A Survey F Mohr, JN van Rijn arXiv preprint arXiv:2201.12150, 2022	63	2022
Metalearning: applications to automated machine learning and data mining P Brazdil, JN Van Rijn, C Soares, J Vanschoren Springer Nature, 2022	63	2022
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	62	2015
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	42	2016
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	41	2023
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	33	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	28	2023

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