Jan N. van Rijn
Jan N. van Rijn
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Cited by
Cited by
OpenML: networked science in machine learning
J Vanschoren, JN Van Rijn, B Bischl, L Torgo
ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014
A survey of deep meta-learning
M Huisman, JN Van Rijn, A Plaat
Artificial Intelligence Review 54 (6), 4483-4541, 2021
Hyperparameter importance across datasets
JN Van Rijn, F Hutter
Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018
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
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
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
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
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
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
OpenML benchmarking suites and the OpenML100
B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ...
stat 1050, 11, 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
The algorithm selection competitions 2015 and 2017
M Lindauer, JN van Rijn, L Kotthoff
Artificial Intelligence 272, 86-100, 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
Metalearning: applications to automated machine learning and data mining
P Brazdil, JN Van Rijn, C Soares, J Vanschoren
Springer Nature, 2022
Learning Curves for Decision Making in Supervised Machine Learning--A Survey
F Mohr, JN van Rijn
arXiv preprint arXiv:2201.12150, 2022
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
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
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
Don’t rule out simple models prematurely: a large scale benchmark comparing linear and non-linear classifiers in OpenML
B Strang, P Putten, JN Rijn, F Hutter
Advances in Intelligent Data Analysis XVII: 17th International Symposium …, 2018
Open algorithm selection challenge 2017: Setup and scenarios
M Lindauer, JN van Rijn, L Kotthoff
Open Algorithm Selection Challenge 2017, 1-7, 2017
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