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Jan N. van Rijn
Jan N. van Rijn
Leiden University
Geverifieerd e-mailadres voor liacs.leidenuniv.nl - Homepage
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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
16142014
A survey of deep meta-learning
M Huisman, JN Van Rijn, A Plaat
Artificial Intelligence Review 54 (6), 4483-4541, 2021
4222021
Hyperparameter importance across datasets
JN Van Rijn, F Hutter
Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018
3052018
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
164*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
1252018
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
1112021
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
1102013
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
1052015
OpenML benchmarking suites and the OpenML100
B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ...
stat 1050 (11), 97, 2017
872017
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
842018
Learning curves for decision making in supervised machine learning: a survey
F Mohr, JN van Rijn
Machine Learning 113 (11), 8371-8425, 2024
782024
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
762014
Metalearning: applications to automated machine learning and data mining
P Brazdil, JN Van Rijn, C Soares, J Vanschoren
Springer Nature, 2022
752022
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
632015
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
572024
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
452016
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
412021
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
402022
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
382023
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Artikelen 1–20