Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019 | 1941 | 2019 |
OpenML: networked science in machine learning J Vanschoren, JN Van Rijn, B Bischl, L Torgo ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014 | 1400 | 2014 |
Meta-learning: A survey J Vanschoren arXiv preprint arXiv:1810.03548, 2018 | 756 | 2018 |
Meta-learning J Vanschoren Automated machine learning: methods, systems, challenges, 35-61, 2019 | 360 | 2019 |
An open source AutoML benchmark P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren arXiv preprint arXiv:1907.00909, 2019 | 288 | 2019 |
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 | 276 | 2016 |
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 | 167 | 2015 |
Importance of tuning hyperparameters of machine learning algorithms HJP Weerts, AC Mueller, J Vanschoren arXiv preprint arXiv:2007.07588, 2020 | 147 | 2020 |
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 | 144 | 2013 |
Selecting classification algorithms with active testing R Leite, P Brazdil, J Vanschoren Machine Learning and Data Mining in Pattern Recognition: 8th International …, 2012 | 136 | 2012 |
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 | 135 | 2012 |
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 | 131 | 2016 |
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 | 123 | 2018 |
Openml benchmarking suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... arXiv preprint arXiv:1708.03731, 2017 | 118 | 2017 |
Meta-features for meta-learning A Rivolli, LPF Garcia, C Soares, J Vanschoren, AC de Carvalho Knowledge-Based Systems 240, 108101, 2022 | 117 | 2022 |
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 | 102 | 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 | 100 | 2013 |
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 | 94 | 2018 |
Dataperf: Benchmarks for data-centric ai development M Mazumder, C Banbury, X Yao, B Karlaš, W Gaviria Rojas, S Diamos, ... Advances in Neural Information Processing Systems 36, 2024 | 92 | 2024 |
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 | 89 | 2021 |