Uni-Logo

2018

  • Lindauer, M. and van Rijn, J. N. and Kotthoff, L. (arXiv)(bib)
    The Algorithm Selection Competition Series 2015-17
    In: arXiv 1805.01214 (2018)
  • Abdulrahman, S. M. and Brazdil, P. and van Rijn, J. N. and Vanschoren, J. (published)(pdf)(bib)
    Speeding up algorithm selection using average ranking and active testing by introducing runtime
    In: Machine Learning
  • van Rijn, J. N. and Holmes, G. and Pfahringer, B. and Vanschoren, J. (published)(pdf)(bib)
    The online performance estimation framework: heterogeneous ensemble learning for data streams
    In: Machine Learning
  • van Rijn, J.N. and Hutter, F. (arXiv)(published)(bib)
    Hyperparameter Importance Across Datasets
    In: SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018)

2017

  • Lindauer, Marius and van Rijn, Jan N. and Kotthoff, Lars (published)(bib)
    Open Algorithm Selection Challenge 2017: Setup and Scenarios
    In: Proceedings of the Open Algorithm Selection Challenge
  • Bischl, Bernd and Casalicchio, Giuseppe and Feurer, Matthias and Hutter, Frank and Lang, Michel and Mantovani, Rafael G. and van Rijn, Jan N. and Vanschoren, Joaquin (arXiv)(bib)
    OpenML Benchmarking Suites and the OpenML100
    In: arXiv 1708.0373 (2017): 1-6
  • van Rijn, J. N. and Hutter, F. (pdf)(bib)
    An Empirical Study of Hyperparameter Importance Across Datasets
    In: Proceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms (AutoML 2017)

2016

  • Post, Martijn J. and van der Putten, Peter and van Rijn, J. N. (pdf)(bib)
    Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML
    In: Advances in Intelligent Data Analysis XV
  • van Rijn, J. N. (published)(bib)
    Massively Collaborative Machine Learning
    PhD thesis, Leiden University, Leiden, Netherlands

2015

  • Vanschoren, J. and van Rijn, J. and Bischl, B. and Casalicchio, G. and Lang, M. and Feurer, M. (pdf)(bib)
    OpenML: a Networked Science Platform for Machine Learning (Abstract)
    In: ICML 2015 MLOSS Workshop
  • van Rijn, J. N. and Takes, F. W. and Vis, J. K. (pdf)(slides)(bib)
    The Complexity of Rummikub Problems
    In: BNAIC 2015: Proceedings of the 27th Benelux Conference on Artificial Intelligence
  • van Rijn, J. N. and Holmes, G. and Pfahringer, B. and Vanschoren, J. (pdf)(bib)
    Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams
    In: Data Mining (ICDM), 2015 IEEE International Conference on
  • van Rijn, J. N. and Vanschoren, J. (pdf)(bib)
    Sharing RapidMiner Workflows and Experiments with OpenML
    In: Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection (MetaSel)
  • van Rijn, J. N. and Holmes, G. and Pfahringer, B. and Vanschoren, J. (pdf)(poster)(bib)
    Case study on bagging stable classifiers for data streams
    In: BENELEARN 2015
  • Vanschoren, J. and van Rijn, J. N. and Bischl, B. (pdf)(bib)
    Taking machine learning research online with OpenML
    In: Proceedings of the 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
  • van Rijn, J. N. and Abdulrahman, S. M. and Brazdil, P. and Vanschoren, J. (pdf)(bib)
    Fast algorithm selection using learning curves
    In: Advances in Intelligent Data Analysis XIV

2014

  • van Rijn, J. N. and Holmes, G. and Pfahringer, B. and Vanschoren, J. (pdf)(bib)
    Towards meta-learning over data streams
    In: MetaSel 2014
  • van Rijn, J. N. and Vis, J. K. (pdf)(bib)
    Endgame Analysis of Dou Shou Qi
    In: ICGA Journal 37.2 (2014): 120--124
  • Hoogeboom, H. J. and Kosters, W. A. and van Rijn, J. N. and Vis, J. K. (pdf)(bib)
    Acyclic Constraint Logic and Games
    In: ICGA Journal 37.1 (2014): 3--16
  • van Rijn, J. N. and Holmes, G. and Pfahringer, B. and Vanschoren, J. (pdf)(poster)(bib)
    Algorithm Selection on Data Streams
    In: Discovery Science
  • Vanschoren, J. and van Rijn, J. N. and Bischl, B. and Torgo, L. (pdf)(bib)
    OpenML: networked science in machine learning
    In: ACM SIGKDD Explorations Newsletter 15.2 (2014): 49--60

2013

  • van Rijn, J. N. and Umaashankar, V. and Fischer, S. and Bischl, B. and Torgo, L. and Gao, B. and Winter, P. and Wiswedel, B. and Berthold, M.R. and Vanschoren, J. (pdf)(bib)
    A RapidMiner extension for open machine learning
    In: RapidMiner Community Meeting and Conference
  • van Rijn, J. N. and Vis, J. K. (pdf)(slides)(bib)
    Complexity and retrograde analysis of the game Dou Shou Qi
    In: BNAIC 2013: Proceedings of the 25th Benelux Conference on Artificial Intelligence
  • van Rijn, J. N. and Bischl, B. and Torgo, L. and Gao, B. and Umaashankar, V. and Fischer, S. and Winter, P. and Wiswedel, B. and Berthold, M. R. and Vanschoren, J. (pdf)(bib)
    OpenML: A Collaborative Science Platform
    In: Machine Learning and Knowledge Discovery in Databases