Jan N. van Rijn

Contents

• Curriculum Vitae
• Teaching
• Publications
• Academic Visits
• Organization
• Contact Information

Curriculum Vitae

Teaching

Publications

• Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas Mueller, Bernd Bischl, Learning Multiple Defaults for Machine Learning Algorithms (Arxiv pre-print), 2018.
• J. N. van Rijn, F. W. Takes, J. K. Vis, Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas (Mirror), 30th Benelux Conference on Artificial Intelligence (BNAIC 2018), 's-Hertogenbosch, the Netherlands, November 8 – 9, 2018.
• Benjamin Strang, Peter van der Putten, Jan N. van Rijn, Frank Hutter, Don't Rule Out Simple Models Prematurely: a Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML (Mirror), The Seventeenth International Symposium on Intelligent Data Analysis (IDA 2018), 's-Hertogenbosch, the Netherlands, 24 – 26 October 2018
• M. Lindauer, J. N. van Rijn and L. Kotthoff, The Algorithm Selection Competitions 2015 and 2017 (ArXiv pre-print), accepted for Artificial Intelligence Journal, 2018.
• J. N. van Rijn and Frank Hutter, Hyperparameter Importance Across Datasets (ArXiv), Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2018), London, UK, August 19 – 23, 2018.
• S. M. Abdulrahman and P. Brazdil, J. N. van Rijn and J. Vanschoren, Speeding up Algorithm Selection using Average Ranking and Active Testing by Introducing Runtime (Mirror), Machine Learning, January 2018, Volume 107, Issue 1, pp 79-108
• J. N. van Rijn, G. Holmes, B. Pfahriner and J. Vanschoren, The online performance estimation framework: heterogeneous ensemble learning for data streams (Mirror), Machine Learning, January 2018, Volume 107, Issue 1, pp 149-176
• M. Lindauer, J. N. van Rijn and L. Kotthoff, Open Algorithm Selection Challenge 2017: Setup and Scenarios (Mirror), Proceedings of Machine Learning Research, September 2017, Volume 79, pp 1-7
• J. N. van Rijn and Frank Hutter, An Empirical Study of Hyperparameter Importance Across Datasets (Mirror), Proceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms, Skopje, Macedonia, Spetember 2017.
• Martijn J. Post, Peter van der Putten, Jan N. van Rijn, Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML (Mirror), The Fifteenth International Symposium on Intelligent Data Analysis ((IDA 2016), Stockholm, Sweden, 13 – 15 October 2016
• Jan N. van Rijn, Salisu Abdulrahman, Pavel Brazdil and Joaquin Vanschoren, On the Evaluation of Algorithm Selection Problems, 25th Belgian-Dutch Conference on Machine Learning (BENELEARN 2016), 12 – 13 September 2016, Kortrijk, Belgium
• Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren, Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams (Mirror), International Conference on Data Mining (ICDM 2015), Atlantic City, New Jersey, USA, November 18 – 22, 2015
• J. N. van Rijn, F. W. Takes, J. K. Vis, The Complexity of Rummikub Problems (Arxiv), 27th Benelux Conference on Artificial Intelligence (BNAIC 2015), Hasselt, Belgium, 5 – 6 November 2013
• Jan N. van Rijn, Salisu Mamman Abdulrahman, Pavel Brazdil, Joaquin Vanschoren, Fast Algorithm Selection Using Learning Curves (Mirror), The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015), St. Etienne, France, October 22 – 24, 2015
• Jan N. van Rijn, Joaquin Vanschoren, Sharing RapidMiner workflows and experiments with OpenML (Mirror), ECML/PKDD Workshop on Meta-learning and Algorithm Selection (MetaSel 2015), Porto, Portugal, September 7, 2015
• Salisu Mamman Abdulrahman, Pavel Brazdil, Jan N. van Rijn, Joaquin Vanschoren, Algorithm Selection via Meta-learning and Sample-based Active Testing (Mirror), ECML/PKDD Workshop on Meta-learning and Algorithm Selection (MetaSel 2015), Porto, Portugal, September 7, 2015
• Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren, Case Study on Bagging Stable Classifiers for Data Streams, 24nd Belgian-Dutch Conference on Machine Learning (BENELEARN 2015), Delft, Netherlands, June 19, 2015
• J.N. van Rijn and J.K. Vis, Endgame Analysis of Dou Shou Qi (Arxiv), ICGA Journal 38 (2014), pp. 120 – 124.
• Jan van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren, Algorithm Selection on Data Streams (Mirror), 17th International Conference on Discovery Science (DS 2014), Bled, Slovenia, October 8 – 10, 2014
• Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren, Towards Meta-learning over Data Streams (Mirror), ECAI Workshop on Meta-learning and Algorithm Selection (MetaSel 2014), Prague, Czech Republic, August 19, 2014
• H.J. Hoogeboom, W.A. Kosters, J. N. van Rijn and J.K. Vis, Acyclic Constraint Logic and Games (Arxiv), ICGA Journal 37 (2014), pp. 3 – 16
• J. Vanschoren, J.N. van Rijn, B. Bischl and L. Torgo, OpenML: networked science in machine learning (Mirror), ACM SIGKDD Explorations Newsletter 15 (2013), pp. 49 – 60
• J.N. van Rijn, J.K. Vis, Complexity and Retrograde Analysis of the Game Dou Shou Qi (Mirror) 25th Benelux Conference on Artificial Intelligence (BNAIC 2013), Delft, The Netherlands, 7 – 8 November 2013
• J.N. van Rijn, B. Bischl, L. Torgo, B. Gao, V. Umaashankar, S. Fischer, P. Winter, B. Wiswedel, M.R. Berthold and J. Vanschoren, OpenML: A Collaborative Science Platform (Mirror), European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013 (ECML/PKDD 2013), Prague, Czech Republic, September 23 – 27, 2013
• J.N. van Rijn, V. Umaashankar, S. Fischer, B. Bischl, L. Torgo, B. Gao, P. Winter, B. Wiswedel, M.R. Berthold and J. Vanschoren, A RapidMiner extension for Open Machine Learning, 4th RapidMiner Community Meeting and Conference (RCOMM 2013), Porto, Portugal, August 27 – 30, 2013
• J.N. van Rijn and J. Vanschoren, OpenML: An Open Science Platform for Machine Learning, 22nd Belgian-Dutch Conference on Machine Learning (BENELEARN 2013), Nijmegen, Netherlands, June 3, 2013

Academic Visits

Organization

Open Machine Learning 2016

from 14 Mar 2016 through 18 Mar 2016
OpenML is a networked science platform that aims to connect and organize all this knowledge online, linking data, algorithms, results and people into a coherent whole so that scientists and practitioners can easy build on prior work and collaborate in real time online.

Contact Information

Postal address

Visiting address