Jan van Rijn
Assistant professor
- Name
- Dr. J.N. van Rijn
- Telephone
- +31 71 527 2727
- j.n.van.rijn@liacs.leidenuniv.nl
- ORCID iD
- 0000-0003-2898-2168
Jan N. van Rijn holds a tenured position as assistant professor at Leiden University, where he works in the computer science department (LIACS) and Automated Design of Algorithms cluster (ADA). His research interests include trustworthy artificial intelligence, automated machine learning (AutoML) and metalearning.
More information about Jan van Rijn
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See also
Former PhD's
Jan N. van Rijn holds a tenured position as assistant professor at Leiden University, where he works in the computer science department (LIACS) and Automated Design of Algorithms cluster (ADA). His research interests include trustworthy artificial intelligence, automated machine learning (AutoML) and metalearning. He obtained his PhD in Computer Science in 2016 at Leiden Institute of Advanced Computer Science (LIACS), Leiden University (the Netherlands). During his PhD, he developed OpenML.org, an open science platform for machine learning, enabling sharing of machine learning results. He made several funded research visits to the University of Waikato (New Zealand) and the University of Porto (Portugal). After obtaining his PhD, he worked as a postdoctoral researcher in the Machine Learning lab at the University of Freiburg (Germany), headed by Prof. Dr. Frank Hutter, after which he moved to work as a postdoctoral researcher at Columbia University in the City of New York (USA). His research aim is to democratize access to machine learning and artificial intelligence across societal institutions, by developing knowledge and tools that support domain experts. He is one of the authors of the book ‘Metalearning: Applications to Automated Machine Learning and Data Mining’ (published by Springer).
Assistant professor
- Science
- Leiden Inst of Advanced Computer Science
- Huisman M., Plaat A. & Rijn J.N. van (2024), Subspace adaptation prior for few-shot learning, Machine Learning 113: 725–752.
- König H.M.T., Bosman A.W., Hoos H.H. & Rijn J.N. van (2024), Critically assessing the state of the art in neural network verification, Journal of Machine Learning Research 25(12): 1-35.
- König H.M.T., Hoos H.H. & Rijn J.N. van (2024), Accelerating adversarially robust model selection for deep neural networks via racing, Proceedings of the AAAI Conference on Artificial Intelligence. 38th AAAI Conference on Artificial Intelligence (AAAI-24) 19 February 2024 - 27 February 2024. Proceedings of the AAAI Conference on Artificial Intelligence no. 38. Washington, DC, USA: AAAI Press. 21267-21275.
- Huisman M., Moerland T.M., Plaat A. & Rijn J.N. van (2023), Are LSTMs good few-shot learners?, Machine Learning 112: 4635–4662.
- König H.M.T., Bosman A.W., Hoos H.H. & Rijn J.N. van (2023), Critically assessing the state of the art in CPU-based local robustness verification. Pedroza G., Huang X., Chen X., Theodorou A., Hernandez-Orallo J., Castillo-Effen M., Mallah R. & McDermid J. (Eds.), Proceedings of the workshop on artificial intelligence safety 2023 (SafeAI 2023). SafeAI 2023: Workshop on Artificial Intelligence Safety 13 February 2023 - 14 February 2023. CEUR Workshop Proceedings no. 3381: CEUR-WS.
- Schlender T., Viljanen M., Rijn J.N. van, Mohr F., Peijnenburg W.J.G.M., Hoos H.H., Rorije E. & Wong A. (2023), The bigger fish: a comparison of meta-learning QSAR models on low-resourced aquatic toxicity regression tasks, Environmental Science and Technology 57(46): 17818-17830.
- Huisman M., Plaat A & Rijn J.N. van (2022), Stateless neural meta-learning using second-order gradients, Machine Learning 111: 3227–3244.
- König H.M.T., Hoos H.H. & Rijn J.N. van (2022), Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio, Machine Learning 111: 4565-4584.
- Moussa C., Rijn J.N. van, Bäck T.H.W. & Dunjko V. (2022), Hyperparameter importance of quantum neural networks across small datasets. Pascal P. & Ienco D. (Eds.), Discovery Science. International Conference on Discovery Science 2022 10 October 2022 - 12 October 2022 no. 13601. Cham: Springer. 32-46.
- Soomlek S. & Rijn J.N. van Bonsangue M.M. (2021), Automatic human-like detection of code smells. Soares C. & Torgo L. (Eds.), Proceedings of the 24th International Conference Discovery Science (DS 2021), Halifax, NS, Canada, October 11-13, 2021. 24th International Conference on Discovery Science, DS 2021 11 October 2021 - 13 October 2021 no. Lecture Notes in Computer Science . Cham: Springer. 19-28.
- Salinas N.R.P., Baratchi M., Rijn J.N. van & Vollrath A. (2021), Automated machine learning for satellite data: integrating remote sensing pre-trained models into AutoML systems. Dong Y., Kourtellis N., Hammer B. & Lozano J.A. (Eds.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Joint European Conference on Machine Learning and Knowledge Discovery in Databasis. ECML PKDD 2021 13 September 2021 - 17 September 2021 no. 12979. Cham: Springer International Publishing. 447-462.
- Huisman M., Rijn J.N. van & Plaat A. (2021), A preliminary study on the feature representations of transfer learning and gradient-based meta-learning techniques, Fifth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems. Fifth Workshop on Meta-Learning 13 December 2021 - 13 December 2021.
- Huisman M., Rijn J.N. van & Plaat A. (2021), A survey of deep meta-learning, Artificial Intelligence Review 54(6): 4483-4541.
- El Baz A., Guyon I., Liu Z., Rijn J.N. van, Treguer S. & Vanschoren J. (2021), Advances in MetaDL: AAAI 2021 Challenge and Workshop. In: Gyuon I., Rijn J.N. van, Treguer S. & Vanschoren J. (Eds.) Proceedings of machine learning research. CoRR no. 140: Microtome Publishing. 1-16.
- Gijsbers P., Pfisterer F., Rijn J.N. van, Bischl B. & Vanschoren J. (2021), Meta-learning for symbolic hyperparameter defaults. Chicano F. (Ed.), GECCO '21: Proceedings of the genetic and evolutionary computation conference companion. GECCO '21: Genetic and Evolutionary Computation Conference 10 July 2021 - 14 July 2021. New York: ACM. 151-152.
- Mohr F. & Rijn J.N. van (2021), Towards model selection using learning curve cross-validation. In: 8th ICML Workshop on automated machine learning (AutoML)..
- Pfisterer F., Rijn J.N. van, Probst P., Müller A.C. & Bischl B. (2021), Learning multiple defaults for machine learning algorithms. Chicano F. (Ed.), GECCO '21: Proceedings of the genetic and evolutionary computation conference companion. GECCO '21: Genetic and Evolutionary Computation Conference 10 July 2021 - 14 July 2021. New York: ACM. 241-242.
- König H.M.T., Hoos H.H. & Rijn J.N. van (2021), Speeding up neural network verification via automated algorithm configuration, ICLR Workshop on Security and Safety in Machine Learning Systems. Workshop Security and Safety in Machine Learning Systems 7 May 2021 - 7 May 2021.
- König H.M.T., Hoos H.H. & Rijn J.N. van (2020), Towards algorithm-agnostic uncertainty estimation: predicting classification error in an automated machine learning setting, ICML Workshop on automated machine learning. 7th ICML Workshop on Automated Machine Learning (AutoML 2020) 18 July 2020 - 18 July 2020.
- Sharma A., Rijn J.N. van, Hutter F. & Müller A. (2019), Hyperparameter Importance for Image Classification by Residual Neural Networks. Kralj Novak P., Smuc T. & Dzeroski S. (Eds.), Proceedings of the 22nd International Conference on Discovery Science, DS 2019. 22nd International Conference on Discovery Science (DS 2019) 28 October 2019 - 30 October 2019 no. 11828: Springer. 112-126.
- Sadawi N., Olier I., Vanschoren J., Rijn J.N. van, Besnard J., Bickerton R., Grosan C., Soldatova L. & King R.D. (2019), Multi-task learning with a natural metric for quantitative structure activity relationship learning, Journal of Cheminformatics 11: 68.
- Lindauer M., Rijn J.N. van & Kotthoff L. (2019), The algorithm selection competitions 2015 and 2017, Artificial Intelligence 272: 86-100.
- Rijn J.N. van, Takes F.W. & Vis J.K. (2019), Computing and Predicting Winning Hands in the Trick-Taking Game of Klaverjas. Atzmueller M. & Duivesteijn W. (Eds.), BNAIC 2018: Artificial Intelligence. 30th Benelux Conference on Artificial Intelligence (BNAIC) 8 November 2018 - 9 November 2018 no. 1021. Cham: Springer. 106-120.
- Abdulrahman S.M., Brazdil P., Rijn J.N. van & Vanschoren J. (2018), Speeding up algorithm selection using average ranking and active testing by introducing runtime, Machine Learning 107(1): 79-108.
- Rijn J.N. van, Holmes G., Pfahringer B. & Vanschoren J. (2018), The online performance estimation framework: heterogeneous ensemble learning for data streams, Machine Learning 107(1): 149-176.
- Rijn J.N. van, Takes F.W. & Vis J.K. (2018), Computing and predicting winning hands in the trick-taking game of Klaverjas. Atzmueller M. & Duivesteijn W. (Eds.), 30th Benelux Conference on Artificial Intelligence. 30th Benelux Conference on Artificial Intelligence (BNAIC) 8 November 2018 - 9 November 2018 207-222.
- Strang B., Putten P.W.H. van der, Rijn J.N. van & Hutter F. (2018), Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML. Duivesteijn W., Siebes A. & Ukkonen A. (Eds.), Advances in Intelligent Data Analysis XVII 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings. International Symposium on Intelligent Data Analysis IDA 2018 24 October 2018 - 26 October 2018 no. Lecture Notes in Computer Science, volume 11191. Cham: Springer. 303-315.
- Rijn J.N. van (19 December 2016), Massively collaborative machine learning (Dissertatie. Leiden Institute of Advanced Computer Science (LIACS), Science, Leiden University) IPA Dissertation Series no. 2016-14. Supervisor(s) and Co-supervisor(s): Kok J.N., Knobbe A.J. & Vanschoren J.
- Rijn J.N. van, Holmes G., Pfahringer B. & Vanschoren J. (2015), Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. Aggarwal C., Zhou Z.H., Tuzhilin A., Xiong H. & Wu X. (Eds.), 2015 IEEE International Conference on Data Mining. ICDM 2015 18 November 2015 - 22 November 2015: IEEE.
- Rijn J.N. van, Holmes G., Pfahringer B. & Vanschoren J. (2015), Case Study on Bagging Stable Classifiers for Data Streams. Benelearn 2015 19 June 2015 - 19 June 2015.
- Rijn J.N. van & Vanschoren J. (2015), Sharing RapidMiner workflows and experiments with OpenML. Vanschoren J., Brazdil P., Giraud-Carrier C. & Kotthoff L. (Eds.), MetaSel'15 Proceedings of the 2015 International Conference on Meta-Learning and Algorithman. MetaSel 2015 7 September 2015 - 7 September 2015 no. 1455. Aachen, Germany: CEUR-WS.org. 93-103.
- Vis J.K., Rijn J.N. van & Takes F.W. (2015), The Complexity of Rummikub Problems, Proceedings of the 27th Benelux Conference on Artificial Intelligence (BNAIC 2015). The 27th Benelux Conference on Artificial Intelligence (BNAIC 2015) 5 November 2015 - 6 November 2015: Benelux Association for Artificial Intelligence.
- Vanschoren J., Rijn J.N. van & Bischl B. (2015), Taking machine learning research online with OpenML. Fan W., Bifet A., Yang Q. & Yu P.S. (Eds.), BIGMINE'15 Proceedings of the 4th International Conference on Big Data, Streams and Heterogeneous Source Minging: Algorithms, Systems, Programming Models and Applications. BigMine 2015 10 August 2015 - 10 August 2015 no. 41: Proceedings of Machine Learniing Research. 1-4.
- Rijn J.N. van, Abdulrahman S.M., Brazdil P. & Vanschoren J. (2015), Fast Algorithm Selection Using Learning Curves, Proceedings 14th International Symposium, IDA 2015. 14th International Symposium, IDA 2015 22 October 2015 - 24 October 2015.
- Abdulrahman S.M., Brazdil P., Rijn J.N. van & Vanschoren J. (2015), Algorithm Selection via Meta-learning and Sample-based Active Testing, Proceedings MetaSel 2015. MetaSel 2015 7 September 2015 - 7 September 2015.
- Rijn J.N. van, Holmes G., Pfahringer B. & Vanschoren J. (2014), Towards Meta-learning over Data Streams, Proceedings MetaSel 2014. MetaSel 2014 19 August 2014 - 19 August 2014.
- Rijn J.N. van, Holmes G., Pfahringer B. & Vanschoren J. (2014), Algorithm Selection on Data Streams, Proceedings Discovery Science. Discovery Science no. LNCS 8777 325-336.
- Vanschoren J., Rijn J.N., Bischl B. & Torgo L. (2014), OpenML: networked science in machine learning, SIGKDD Explorations : .
- Van Rijn J.N. & Vis J.K. (2014), Endgame Analysis of Dou Shou Qi, ICGA Journal 38: 120–124.
- Hoogeboom H.J., Kosters W.A., Rijn J.N. van & Vis J.K. (2014), Acyclic Constraint Logic and Games, ICGA Journal 37(1): 3-16.
- Rijn J.N. van & Vis J.K. (2013), Complexity and Retrograde Analysis of the Game Dou Shou Qi. Hindriks K., Weerdt M. de, Riemsdijk B. van & Warnier M. (Eds.), Proceedings of the 25th Benelux Conference on Artificial Intelligence. 25th Benelux Conference on Artificial Intelligence (BNAIC 2013) 239-246.
- Rijn J.N. van, Umaashankar V., Fischer S., Bischl B., Torgo L., Gao B., Winter P., Wiswedel B., Berthold M.R. & Vanschoren J. (2013), A RapidMiner extension for Open Machine Learning. Fischer S., Mierswa I., Mendes Moreira J. & Soares C. (Eds.), RapidMiner Community Meeting and Conference 2013. : Shaker Verlag. 59-70.
- Rijn J.N. van, Bischl B., Torgo L., Gao B., Umaashankar V., Fischer S., Winter P., Wiswedel B., Berthold M.R. & Vanschoren J. (2013), OpenML: A Collaborative Science Platform. Blockeel H., Kersting K., Nijssen Siegfried & Zelezný Filip (Eds.), Machine Learning and Knowledge Discovery in Databases. no. Lecture Notes in Computer Science: Springer-Verlag. 645-649.
- Rijn J.N. van & Vanschoren J. (2013), OpenML: An Open Science Platform for Machine Learning, Proceedings Benelearn 2013. .