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
Hyperparameter importance across datasets
JN Van Rijn, F Hutter
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
A survey of deep meta-learning
M Huisman, JN Van Rijn, A Plaat
Artificial Intelligence Review 54 (6), 4483-4541, 2021
OpenML benchmarking suites and the OpenML100
B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ...
arXiv:1708.03731, 2017
The online performance estimation framework: heterogeneous ensemble learning for data streams
JN van Rijn, G Holmes, B Pfahringer, J Vanschoren
Machine Learning 107 (1), 149-176, 2018
Fast algorithm selection using learning curves
JN Rijn, SM Abdulrahman, P Brazdil, J Vanschoren
International symposium on intelligent data analysis, 298-309, 2015
OpenML: A collaborative science platform
JN Rijn, B Bischl, L Torgo, B Gao, V Umaashankar, S Fischer, P Winter, ...
Joint european conference on machine learning and knowledge discovery in …, 2013
Algorithm selection on data streams
JN Rijn, G Holmes, B Pfahringer, J Vanschoren
International Conference on Discovery Science, 325-336, 2014
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 (1), 79-108, 2018
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
Openml-python: an extensible python api for openml
M Feurer, JN Van Rijn, A Kadra, P Gijsbers, N Mallik, S Ravi, A Müller, ...
The Journal of Machine Learning Research 22 (1), 4573-4577, 2021
The algorithm selection competitions 2015 and 2017
M Lindauer, JN van Rijn, L Kotthoff
Artificial Intelligence 272, 86-100, 2019
Does feature selection improve classification? a large scale experiment in OpenML
MJ Post, P Putten, JN Rijn
International Symposium on Intelligent Data Analysis, 158-170, 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
Open algorithm selection challenge 2017: Setup and scenarios
M Lindauer, JN van Rijn, L Kotthoff
Open Algorithm Selection Challenge 2017, 1-7, 2017
Massively collaborative machine learning
JN van Rijn
Leiden University, 2016
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
International Symposium on Intelligent Data Analysis, 303-315, 2018
Algorithm selection via meta-learning and sample-based active testing
SM Abdulrhaman, P Brazdil, JN Van Rijn, J Vanschoren
An Empirical Study of Hyperparameter Importance Across Datasets.
JN Van Rijn, F Hutter
AutoML@ PKDD/ECML, 91-98, 2017
Metalearning: Applications to Automated Machine Learning and Data Mining
P Brazdil, JN van Rijn, C Soares, J Vanschoren
Springer Nature, 2022
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