Joaquin Vanschoren

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Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl
Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
Lars KotthoffUniversity of WyomingVerified email at uwyo.edu
André Carlos Ponce de Leon Ferreira...Artificial Intelligence Professor, University of São PauloVerified email at icmc.usp.br
Carlos SoaresAssociate Professor, Faculdade de Engenharia, Universidade do Porto / Fraunhofer AICOS / LIACCVerified email at fep.up.pt
Hendrik BlockeelDepartment of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
Rafael Gomes MantovaniFederal Technology University - Paraná, campus of ApucaranaVerified email at utfpr.edu.br
Bernhard PfahringerProfessor of Computer Science, University of WaikatoVerified email at waikato.ac.nz
Geoffrey HolmesUniversity of WaikatoVerified email at waikato.ac.nz
Pieter GijsbersEindhoven University of TechnologyVerified email at tue.nl
Larisa SoldatovaProfessor, Goldsmiths, University of LondonVerified email at gold.ac.uk
Luís TorgoCanada Research Chair, Professor, Faculty of Computer Science, Dalhousie UniversityVerified email at dal.ca
Andre Luis Debiaso RossiUniversidade Estadual Paulista (UNESP)Verified email at unesp.br
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Bo GaoHuaweiVerified email at huawei.com
Andreas Christian MuellerMicrosoftVerified email at microsoft.com
Salisu Mamman Abdulrahman (PhD)Faculty Member, Kano University of Science and Technology, Wudil, KanoVerified email at kustwudil.edu.ng
Janek ThomasLudwig-Maximilians-University MunichVerified email at stat.uni-muenchen.de
Matthias FeurerLMU Munich & Munich Center for Machine LearningVerified email at stat.uni-muenchen.de

Joaquin Vanschoren

Eindhoven University of Technology

Verified email at tue.nl - Homepage

Artificial Intelligence Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Automated machine learning: methods, systems, challenges F Hutter, L Kotthoff, J Vanschoren Springer Nature, 2019	1606	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	1238	2014
Meta-learning: A survey J Vanschoren arXiv preprint arXiv:1810.03548, 2018	681	2018
Meta-learning J Vanschoren Automated machine learning: methods, systems, challenges, 35-61, 2019	296	2019
An open source AutoML benchmark P Gijsbers, E LeDell, J Thomas, S Poirier, B Bischl, J Vanschoren arXiv preprint arXiv:1907.00909, 2019	252	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	242	2016
Openml benchmarking suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... arXiv preprint arXiv:1708.03731, 2017	144	2017
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	137	2015
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	137	2013
Selecting classification algorithms with active testing R Leite, P Brazdil, J Vanschoren Machine Learning and Data Mining in Pattern Recognition: 8th International …, 2012	132	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	131	2012
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	114	2018
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	112	2016
Importance of tuning hyperparameters of machine learning algorithms HJP Weerts, AC Mueller, J Vanschoren arXiv preprint arXiv:2007.07588, 2020	111	2020
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	95	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	89	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	85	2018
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	77	2007
Data Augmentation using Conditional Generative Adversarial Networks for Leaf Counting in Arabidopsis Plants. Y Zhu, M Aoun, M Krijn, J Vanschoren, HT Campus BMVC, 324, 2018	76	2018
To tune or not to tune: recommending when to adjust SVM hyper-parameters via meta-learning RG Mantovani, ALD Rossi, J Vanschoren, B Bischl, AC Carvalho 2015 International joint conference on neural networks (IJCNN), 1-8, 2015	72	2015

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