Jan N. van Rijn
Jan N. van Rijn
Columbia University
Verified email at liacs.leidenuniv.nl - Homepage
TitleCited byYear
OpenML: networked science in machine learning
J Vanschoren, JN Van Rijn, B Bischl, L Torgo
ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014
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
Fast algorithm selection using learning curves
JN van Rijn, SM Abdulrahman, P Brazdil, J Vanschoren
International symposium on intelligent data analysis, 298-309, 2015
Algorithm selection on data streams
JN van Rijn, G Holmes, B Pfahringer, J Vanschoren
International Conference on Discovery Science, 325-336, 2014
The elevation of sarcoplasmic reticulum Ca2+-ATPase levels by thyroid hormone in the L6 muscle cell line is potentiated by insulin-like growth factor-I
A Muller, C Van Hardeveld, WS Simonides, J Van Rijn
Biochemical journal 275 (1), 35-40, 1991
Hyperparameter importance across datasets
JN van Rijn, F Hutter
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
Ca2+ homeostasis and fast-type sarcoplasmic reticulum Ca2+-ATPase expression in L6 muscle cells. Role of thyroid hormone
A Muller, C Van Hardeveld, WS Simonides, J Van Rijn
Biochemical Journal 283 (3), 713-718, 1992
OpenML benchmarking suites and the OpenML100
B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ...
arXiv preprint arXiv:1708.03731, 2017
Algorithm selection via meta-learning and sample-based active testing
SM Abdulrhaman, P Brazdil, JN Van Rijn, J Vanschoren
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
Does feature selection improve classification? a large scale experiment in OpenML
MJ Post, P van der Putten, JN van Rijn
International Symposium on Intelligent Data Analysis, 158-170, 2016
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
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
Playing Games: The complexity of Klondike, Mahjong, Nonograms and Animal Chess
JN van Rijn
Massively collaborative machine learning
JN van Rijn
IPA Dissertation Series, 2016
Taking machine learning research online with OpenML
J Vanschoren, JN van Rijn, B Bischl
Proceedings of the 4th International Workshop on Big Data, Streams and …, 2015
A RapidMiner extension for Open Machine Learning
JN Van Rijn, V Umaashankar, S Fischer, B Bischl, L Torgo, B Gao, ...
RCOMM 2013, 2013
Open algorithm selection challenge 2017: Setup and scenarios
M Lindauer, JN van Rijn, L Kotthoff
Open Algorithm Selection Challenge 2017, 1-7, 2017
Complexity and retrograde analysis of the game Dou Shou Qi
JN Van Rijn, JK Vis
Openml: Networked science in machine learning. SIGKDD Explorations 15 (2), 49–60 (2013)
J Vanschoren, JN van Rijn, B Bischl, L Torgo
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