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Joaquin Vanschoren
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Year
Automated machine learning: methods, systems, challenges
F Hutter, L Kotthoff, J Vanschoren
Springer Nature, 2019
17452019
OpenML: networked science in machine learning
J Vanschoren, JN Van Rijn, B Bischl, L Torgo
ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014
13072014
Meta-learning: A survey
J Vanschoren
arXiv preprint arXiv:1810.03548, 2018
7212018
Meta-learning
J Vanschoren
Automated machine learning: methods, systems, challenges, 35-61, 2019
3222019
An open source AutoML benchmark
P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren
arXiv preprint arXiv:1907.00909, 2019
2672019
Aslib: A benchmark library for algorithm selection
B Bischl, P Kerschke, L Kotthoff, M Lindauer, Y Malitsky, A Fréchette, ...
Artificial Intelligence 237, 41-58, 2016
2512016
Openml benchmarking suites
B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ...
arXiv preprint arXiv:1708.03731, 2017
1602017
Effectiveness of random search in SVM hyper-parameter tuning
RG Mantovani, ALD Rossi, J Vanschoren, B Bischl, AC De Carvalho
2015 international joint conference on neural networks (IJCNN), 1-8, 2015
1492015
A survey of intelligent assistants for data analysis
F Serban, J Vanschoren, JU Kietz, A Bernstein
ACM Computing Surveys (CSUR) 45 (3), 1-35, 2013
1412013
Selecting classification algorithms with active testing
R Leite, P Brazdil, J Vanschoren
Machine Learning and Data Mining in Pattern Recognition: 8th International …, 2012
1362012
Experiment databases. A new way to share, organize and learn from experiments
J Vanschoren, H Blockeel, B Pfahringer, G Holmes
Machine learning 87 (2), 127-158, 2012
1312012
Importance of tuning hyperparameters of machine learning algorithms
HJP Weerts, AC Mueller, J Vanschoren
arXiv preprint arXiv:2007.07588, 2020
1232020
Hyper-parameter tuning of a decision tree induction algorithm
RG Mantovani, T Horváth, R Cerri, J Vanschoren, AC De Carvalho
2016 5th Brazilian Conference on Intelligent Systems (BRACIS), 37-42, 2016
1222016
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
1192018
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
982015
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
922013
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
I Olier, N Sadawi, GR Bickerton, J Vanschoren, C Grosan, L Soldatova, ...
Machine Learning 107, 285-311, 2018
902018
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
782018
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
772021
Experiment databases: Towards an improved experimental methodology in machine learning
H Blockeel, J Vanschoren
European Conference on Principles of Data Mining and Knowledge Discovery, 6-17, 2007
772007
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