| OpenML: networked science in machine learning J Vanschoren, JN Van Rijn, B Bischl, L Torgo ACM SIGKDD Explorations Newsletter 15 (2), 49-60, 2014 | 1884 | 2014 |
| A survey of deep meta-learning M Huisman, JN Van Rijn, A Plaat Artificial Intelligence Review 54 (6), 4483-4541, 2021 | 539 | 2021 |
| Hyperparameter importance across datasets JN Van Rijn, F Hutter Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 381 | 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 | 270* | 2021 |
| 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 | 150 | 2021 |
| Automated machine learning: past, present and future M Baratchi, C Wang, S Limmer, JN Van Rijn, H Hoos, T Bäck, M Olhofer Artificial intelligence review 57 (5), 122, 2024 | 148 | 2024 |
| 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 | 139 | 2018 |
| Learning curves for decision making in supervised machine learning: a survey F Mohr, JN van Rijn Machine Learning 113 (11), 8371-8425, 2024 | 127 | 2024 |
| 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 | 124 | 2013 |
| Metalearning: Applications to automated machine learning and data mining P Brazdil, JN Van Rijn, C Soares, J Vanschoren Springer Nature, 2022 | 118 | 2022 |
| Fast algorithm selection using learning curves JN van Rijn, SM Abdulrahman, P Brazdil, J Vanschoren International symposium on intelligent data analysis, 298-309, 2015 | 110 | 2015 |
| 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 | 93 | 2018 |
| The algorithm selection competitions 2015 and 2017 M Lindauer, JN van Rijn, L Kotthoff Artificial Intelligence 272, 86-100, 2019 | 85* | 2019 |
| Algorithm selection on data streams JN van Rijn, G Holmes, B Pfahringer, J Vanschoren International conference on discovery science, 325-336, 2014 | 85 | 2014 |
| Fast and informative model selection using learning curve cross-validation F Mohr, JN van Rijn IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (8), 9669-9680, 2023 | 72 | 2023 |
| OpenML benchmarking suites and the OpenML100 B Bischl, G Casalicchio, M Feurer, F Hutter, M Lang, RG Mantovani, ... stat 1050 (11), 97, 2017 | 69 | 2017 |
| 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 | 67 | 2015 |
| 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 1 (3), 2023 | 63 | 2023 |
| Meta-album: Multi-domain meta-dataset for few-shot image classification I Ullah, D Carrión-Ojeda, S Escalera, I Guyon, M Huisman, F Mohr, ... Advances in Neural Information Processing Systems 35, 3232-3247, 2022 | 61 | 2022 |
| Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward D Tuia, K Schindler, B Demir, XX Zhu, M Kochupillai, S Džeroski, ... IEEE Geoscience and Remote Sensing Magazine, 2024 | 58 | 2024 |
Joaquin VanschorenEindhoven University of Technology; Google Deepmind (Visiting)Verified email at tue.nl
