Jan N. van Rijn

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	Semua	Sejak 2020
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Joaquin VanschorenEindhoven University of TechnologyEmail yang diverifikasi di tue.nl
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichEmail yang diverifikasi di stat.uni-muenchen.de
Felix MohrUniversidad de La Sabana - ColombiaEmail yang diverifikasi di unisabana.edu.co
Frank HutterELLIS Institute Tübingen & Professor, University of Freiburg, GermanyEmail yang diverifikasi di cs.uni-freiburg.de
Aske PlaatLeiden UniversityEmail yang diverifikasi di liacs.leidenuniv.nl
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaEmail yang diverifikasi di cs.rwth-aachen.de
Bernhard PfahringerProfessor of Computer Science, University of WaikatoEmail yang diverifikasi di waikato.ac.nz
Geoffrey HolmesUniversity of WaikatoEmail yang diverifikasi di waikato.ac.nz
Matthias FeurerLMU MunichEmail yang diverifikasi di stat.uni-muenchen.de
Andreas Christian MuellerMicrosoftEmail yang diverifikasi di microsoft.com
Matthias KönigLeiden University, RWTH Aachen University, University of OxfordEmail yang diverifikasi di cs.ox.ac.uk
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchEmail yang diverifikasi di ai.uni-hannover.de
Lars KotthoffUniversity of WyomingEmail yang diverifikasi di uwyo.edu
Thomas BäckProfessor of Computer Science, Leiden University; Chief Scientist, NORCE Research Centre, NorwayEmail yang diverifikasi di liacs.leidenuniv.nl
Frank W. TakesAssociate Professor, Leiden UniversityEmail yang diverifikasi di liacs.nl
Annelot BosmanPhD candidate AutoML, Universiteit LeidenEmail yang diverifikasi di liacs.leidenuniv.nl
Walter KostersAssociate professor, Universiteit LeidenEmail yang diverifikasi di liacs.leidenuniv.nl
Ross D. KingProfessor of Machine Intelligence, Chalmers UniversityEmail yang diverifikasi di chalmers.se
Inês GomesPhD Student @ Porto University (FEUP)Email yang diverifikasi di fe.up.pt
Pavel BrazdilProfessor Emeritus, FEP, U.Porto, Member of LIAAD-INESC TEC

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Jan N. van Rijn

Leiden University

Email yang diverifikasi di liacs.leidenuniv.nl - Beranda

Machine Learning Artificial Intelligence Trustworthy AI


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
OpenML: networked science in machine learning J Vanschoren, JN Van Rijn, B Bischl, L Torgo ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014	1632	2014
A survey of deep meta-learning M Huisman, JN Van Rijn, A Plaat Artificial Intelligence Review 54 (6), 4483-4541, 2021	427	2021
Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018	308	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	166*	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	125	2018
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	111	2021
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	110	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	105	2015
OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050 (11), 97, 2017	87	2017
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	84	2018
Learning curves for decision making in supervised machine learning: a survey F Mohr, JN van Rijn Machine Learning 113 (11), 8371-8425, 2024	79	2024
Metalearning: applications to automated machine learning and data mining P Brazdil, JN Van Rijn, C Soares, J Vanschoren Springer Nature, 2022	78	2022
The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019	78*	2019
Algorithm selection on data streams JN van Rijn, G Holmes, B Pfahringer, J Vanschoren Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia …, 2014	77	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	64	2015
Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward D Tuia, K Schindler, B Demir, XX Zhu, M Kochupillai, S Džeroski, ... IEEE Geoscience and Remote Sensing Magazine, 2024	61	2024
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	45	2016
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	41	2022
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	41	2021
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	38	2023

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