Publications ~ Matthijs van Leeuwen

Publications (per type)

In press
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2004
2003
2002

Publications by Year (per type)

In press

Li, Z, Liang, , Shi, J & van Leeuwen, M Cross-Domain Graph Level Anomaly Detection. Transactions on Knowledge and Data Engineering, ACM

2024

	Yang, L & van Leeuwen, M Conditional Density Estimation with Histogram Trees. In: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS 2024), 2024.
	Lenz, O & van Leeuwen, M Directional Anomaly Detection. In: Proceedings of BNAIC/BeNeLearn 2024, 2024.
	Li, Z, Zhu, Y & van Leeuwen, M A Survey on Explainable Anomaly Detection. Transactions on Knowledge Discovery from Data vol.18(1), ACM, 2024.
	Yang, L & van Leeuwen, M Human-guided Rule Learning for ICU Readmission Risk Analysis. In: Proceedings of the Workshop on AI and Data Science for Healthcare (AIDSH) at KDD 2024, 2024.
	Li, Z, Shi, J & van Leeuwen, M Graph Neural Networks based Log Anomaly Detection and Explanation. In: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, pp 306-307, ACM, 2024.

2023

	Kroes, SKS, van Leeuwen, M, Groenwold, RHH & Janssen, MP Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis. Journal of Cybersecurity and Privacy vol.3(4), pp 882-894, MDPI, 2023.
	van Dijk, R, Gawehns, D & van Leeuwen, M WEARDA: recording wearable sensor data for human activity monitoring. Journal of Open Research Software vol.11(1), 2023.
	Vinkenoog, M, Toivonen, J, van Leeuwen, M, Janssen, M & Arvas, M The added value of ferritin levels and genetic markers for the prediction of haemoglobin deferral. Vox Sanguinis vol.118(10), pp 825-834, 2023.
	Li, Z & an Leeuwen, M Explainable Contextual Anomaly Detection using Quantile Regression Forests. Data Mining and Knowledge Discovery, Springer
	Lopez-Martinez-Carrasco, A, Proença, HM, Juarez, JM, van Leeuwen, M & Campos, M Novel approach for phenotyping based on diverse top-k subgroup lists. In: Proceedings of the Conference on Artificial Intelligence In Medicine (AIME 2023), Springer, 2023.
	Lopez-Martinez-Carrasco, A, Proença, HM, Juarez, JM, van Leeuwen, M & Campos, M Discovering Diverse Top-k Characteristic Lists. In: Proceedings of the 21st International Symposium on Intelligent Data Analysis (IDA 2023), Springer, 2023.
	Papagianni, I & van Leeuwen, M Discovering Rule Lists with Preferred Variables. In: Proceedings of the 21st International Symposium on Intelligent Data Analysis (IDA 2023), Springer, 2023.
	van der Arend, B, Verhagen, I, van Leeuwen, M, van der Arend, M, van Casteren, D & Terwindt, G Defining migraine days, based on longitudinal E-diary data. Cephalalgia
	Yang, L, Baratchi, M & van Leeuwen, M Unsupervised Discretization by Two-dimensional MDL-based Histogram. Machine Learning, Springer
	Kroes, SKS, van Leeuwen, M, Groenwold, RHH & Janssen, MP Generating synthetic mixed discrete-continuous health records with mixed sum-product networks. Journal of the American Medical Informatics Association vol.30(1), Oxford University Press, 2023.

2022

	Li, Z & van Leeuwen, M Feature Selection for Fault Detection and Prediction based on Event Log Analysis. ACM SIGKDD Explorations vol.24(2), ACM, 2022.
	Li, Z, Zhu, Y & van Leeuwen, M A Survey on Explainable Anomaly Detection. Technical Report arXiv:2210.06959, arXiv, 2022.
	Spaink, HA, Verhagen, IE, van Leeuwen, M & Terwindt, GM Methodological considerations in predicting migraine attacks using machine learning. In: MTIS 2022 Cephalalgia Abstracts, Sage Publications, 2022.
	Li, Z & van Leeuwen, M Feature Selection for Fault Detection and Prediction based on Log Analysis. In: Proceedings of the international workshop on AI for Manufacturing Workshop at ECMLPKDD 2022, 2022.
	Yang, L, Opdam, T & van Leeuwen, M Histogram-based Probabilistic Rule Lists for Numeric Targets. In: Proceedings of the 20th anniversary Workshop on Knowledge Discovery in Inductive Databases (KDID 2022) at ECMLPKDD 2022, CEUR Workshop Proceedings, 2022.
	Yang, L & van Leeuwen, M Truly Unordered Probabilistic Rule Sets for Multi-class Classification. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2022), Springer, 2022.
	Vinkenoog, M, van Leeuwen, M & Janssen, M Explainable hemoglobin deferral predictions using machine learning models: interpretation and consequences for the blood supply. Vox Sanguinis
	Proença, HM, Grünwald, P, Bäck, T & van Leeuwen, M Robust subgroup discovery - Discovering subgroup lists using MDL. Data Mining and Knowledge Discovery
	van Rijn, S, Schmitt, S, van Leeuwen, M & Bäck, T Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling. Engineering Optimization
	Yang, L & van Leeuwen, M Probabilistic Rule Sets Ready for Interactive Machine Learning. In: AAAI'22-Workshop on Interactive Machine Learning, 2022.
	Vinkenoog, M, Steenhuis, M, ten Brinke, A, van Hasselt, C, Janssen, M, van Leeuwen, M, Swaneveld, F, Vrielink, H, van de Watering, L, Quee, F, van cen Hurk, K, Rispens, T, Hogema, B & van der Schoot, E Associations between symptoms, donor characteristics and IgG antibody response in 2082 COVID-19 convalescent plasma donors. Frontiers in Immunology, Frontiers

2021

	Kroes, SKS, Janssen, MP, Groenwold, RHH & van Leeuwen, M Evaluating privacy of individuals in medical data. Health Informatics Journal, SAGE Publications
	Marx, A, Yang, L & van Leeuwen, M Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. In: Proceedings of the SIAM Conference on Data Mining 2021 (SDM'21), SIAM, 2021.
	Proença, HM, Bäck, T & van Leeuwen, M Robust subgroup discovery. Technical Report arXiv:2103.13686, arXiv, 2021.
	van Rijn, S, Schmitt, S, van Leeuwen, M & Bäck, T Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling. Technical Report arXiv:2103.03280, arXiv, 2021.
	Kapoor, S, Saxena, DK & van Leeuwen, M Online Summarization of Dynamic Graphs using Subjective Interestingness for Sequential Data. Data Mining and Knowledge Discovery vol.35(1), pp 88-126, 2021. (ECML PKDD journal track)
	Marx, A, Yang, L & van Leeuwen, M Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. Technical Report arXiv:2101.05009, arXiv, 2021.

2020

	Gawehns, D & van Leeuwen, M Social Fluidity in Children's Face-to-Face Interaction Networks. In: Proceedings of the Graph Embedding and Mining (GEM) Workshop at ECML PKDD 2020, 2020.
	Proença, HM, Grünwald, P, Bäck, T & van Leeuwen, M Discovering Outstanding Subgroup Lists for Numeric Targets using MDL. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2020), Springer, 2020.
	Vinkenoog, M, van den Hurk, K, van Kraaij, M, van Leeuwen, M & Janssen, M First results of a ferritin-based blood donor deferral policy in the Netherlands. Transfusion vol.60(8), pp 1785-1792, Wiley, 2020.
	Proença, HM, Grünwald, P, Bäck, T & van Leeuwen, M Discovering outstanding subgroup lists for numeric targets using MDL. Technical Report arXiv:2006.09186, arXiv, 2020.
	Yang, L, Baratchi, M & van Leeuwen, M Unsupervised Discretization by Two-dimensional MDL-based Histogram. Technical Report arXiv:2006.01893, arXiv, 2020.
	Faas, M & van Leeuwen, M Vouw: Geometric Pattern Mining using the MDL Principle. In: Proceedings of the Eighteenth International Symposium on Intelligent Data Analysis (IDA 2020), Springer, 2020.
	Gautrais, C, Cellier, P, van Leeuwen, M & Termier, A Widening for MDL-based Retail Signature Discovery. In: Proceedings of the Eighteenth International Symposium on Intelligent Data Analysis (IDA 2020), Springer, 2020.
	Kapoor, S, Saxena, DK & van Leeuwen, M Discovering Subjectively Interesting Multigraph Patterns. Machine Learning, pp 1-28, Springer
	Proença, HM & van Leeuwen, M Interpretable multiclass classification by MDL-based rule lists. Information Sciences vol.512, pp 1372-1393, Elsevier, 2020.

2019

	Faas, M & van Leeuwen, M Vouw: Geometric Pattern Mining using the MDL Principle. Technical Report arXiv:1911.09587, arXiv, 2019.
	Vinkenoog, M, Janssen, M & van Leeuwen, M Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories. In: Proceedings of 4th Workshop on Advanced Analytics and Learning on Temporal Data at ECMLPKDD 2019, Springer, 2019.
	Gawehns, D, Veiga, G & van Leeuwen, M Focus on dynamics: a proof of principle in exploratory data mining of face-to-face interactions. In: Proceedings of the 5th International Conference on Computational Social Science (IC2S2), 2019. (Poster presentation)
	Proença, HM & van Leeuwen, M Interpretable multiclass classification by MDL-based rule lists. Technical Report arXiv:1905.00328, arXiv, 2019.
	van Leeuwen, M, Chau, DH, Vreeken, J, Shahaf, D & Faloutsos, C Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12, Iss. 1): Introduction by the Guest Editors. Transactions on Knowledge Discovery from Data vol.13(1), ACM, 2019.

2018

	Proença, HM, Klijn, R, Bäck, T & van Leeuwen, M Identifying flight delay patterns using diverse subgroup discovery. In: Proceedings of the Symposium Series on Computational Intelligence (SSCI'18), IEEE, 2018.
	van Os, H, Ramos, L, Hilbert, A, van Leeuwen, M, van Walderveen, M, Kruyt, N, Dippel, D, Steyerberg, E, van der Schaaf, I, Lingsma, H, Schonewille, W, Majoie, C, Olabarriaga, S, Zwinderman, K, Venema, E, Marquering, H & Wermer, M Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms. Frontiers in Neurology vol.9(784), Frontiers, 2018.
	van Rijn, S, van Leeuwen, M, Schmitt, S, Olhofer, M & Bäck, T Multi-Fidelity Surrogate Model Approach to Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'18), ACM, 2018.
	van Leeuwen, M, Chau, DH, Vreeken, J, Shahaf, D & Faloutsos, C Editorial: TKDD Special Issue on Interactive Data Exploration and Analytics. Transactions on Knowledge Discovery from Data vol.12(1), ACM, 2018.

2017

	Paramonov, S, van Leeuwen, M & De Raedt, L Relational Data Factorization. Machine Learning vol.106(12), pp 1867-1904, Springer, 2017.
	Ukkonen, A, Dzyuba, V & van Leeuwen, M Explaining Deviating Subsets through Explanation Networks. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'17), Springer, 2017.
	Dzyuba, V, van Leeuwen, M & De Raedt, L Flexible constrained sampling with guarantees for pattern mining. Data Mining and Knowledge Discovery vol.31(5), pp 1266-1293, Springer, 2017. (ECMLPKDD'17 Special Issue)
	Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'17). 2017.
	Le Van, T, Nijssen, S, van Leeuwen, M & De Raedt, L Semiring Rank Matrix Factorisation. Transactions on Knowledge and Data Engineering vol.29(8), pp 1737-1750, IEEE, 2017.
	Dzyuba, V & van Leeuwen, M Learning what matters – Sampling interesting patterns. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'17), pp 534-546, Springer, 2017.
	Dzyuba, V & van Leeuwen, M Learning what matters - Sampling interesting patterns. Technical Report arXiv:1702.01975, arXiv, 2017.

2016

	van Stein, B, van Leeuwen, M, Wang, H, Purr, S, Kreissl, S, Meinhardt, J & Bäck, T Towards Data Driven Process Control in Manufacturing Car Body Parts. In: Proceedings of IEEE International Conference on Computational Science and Computational Intelligence (IEEE CSCI-ISBD'16), IEEE, 2016.
	van Rijn, S, Wang, H, van Leeuwen, M & Bäck, T Evolving the Structure of Evolution Strategies. In: Proceedings of IEEE Symposium Series on Computational Intelligence (IEEE SSCI'16), IEEE, 2016.
	van Stein, B, van Leeuwen, M & Bäck, T Local Subspace-Based Outlier Detection using Global Neighbourhoods. In: Proceedings of IEEE International Conference on Big Data (IEEE BigData'16), IEEE, 2016.
	van Stein, B, van Leeuwen, Mv & Bäck, T Local Subspace-Based Outlier Detection using Global Neighbourhoods. Technical Report arXiv:1611.00183, arXiv, 2016.
	Dzyuba, V, van Leeuwen, M & De Raedt, L Flexible constrained sampling with guarantees for pattern mining. Technical Report arXiv:1610.09263, arXiv, 2016.
	van Rijn, S, Wang, H, van Leeuwen, M & Bäck, T Evolving the Structure of Evolution Strategies. Technical Report arXiv:1610.05231, arXiv, 2016.
	van Leeuwen, M & Ukkonen, A Expect the Unexpected - On the Significance of Subgroups. In: Proceedings of Discovery Science (DS'16), pp 51-66, Springer, 2016.
	Le Van, T, van Leeuwen, M, Fierro, AC, De Maeyer, D, Van den Eynden, J, Verbeke, L, De Raedt, L, Marchal, K & Nijssen, S Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinformatics vol.32(17), pp 445-454, Oxford University Press, 2016.
	Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'16). 2016.
	Copmans, D, Meinl, T, Dietz, C, van Leeuwen, M, Ortmann, J, Berthold, M & de Witte, PAM A KNIME-based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules. Journal of Biomolecular Screening vol.21(5), pp 427-436, SAGE Publishing, 2016.
	van Leeuwen, M & Galbrun, E Association Discovery in Two-View Data (extended abstract). In: TKDE Poster Track of ICDE 2016, IEEE, 2016.
	van Leeuwen, M, De Bie, T, Spyropoulou, E & Mesnage, C Subjective Interestingness of Subgraph Patterns. Machine Learning vol.105(1), pp 41-75, Springer, 2016.

2015

	Aksehirli, E, Nijssen, S, van Leeuwen, M & Goethals, B Finding Subspace Clusters using Ranked Neighborhoods. In: Proceedings of the ICDM 2015 workshops (HDM workshop), IEEE, 2015.
	Fromont, E, De Bie, T & van Leeuwen, M (eds) Advances in Intelligent Data Analysis XIV (proceedings of IDA 2015). LNCS 9385, Springer, 2015.
	van Leeuwen, M & Cardinaels, L VIPER - Visual Pattern Explorer. Demo paper in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'15), Springer, 2015.
	Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'15). 2015.
	van Leeuwen, M & Galbrun, E Association Discovery in Two-View Data. Transactions on Knowledge and Data Engineering vol.27(12), pp 3190-3202, IEEE, 2015.
	Paramonov, S, van Leeuwen, M, Denecker, M & De Raedt, L An exercise in declarative modeling for relational query mining. In: Proceedings of the 25th International Conference On Inductive Logic Programming (ILP'15), pp 166-182, Springer, 2015.
	Le Van, T, van Leeuwen, M, Nijssen, S & De Raedt, L Rank Matrix Factorisation. In: Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'15), pp 734-746, Springer, 2015.
	van Leeuwen, M & Ukkonen, A Same bang, fewer bucks: efficient discovery of the cost-influence skyline. In: Proceedings of the SIAM Conference on Data Mining 2015 (SDM'15), SIAM, 2015.
	van Leeuwen, M, Siebes, A & Vreeken, J Information Theoretic Methods in Data Mining. At the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'14), Nancy, France, 2015.

2014

	Dzyuba, V, van Leeuwen, M, Nijssen, S & De Raedt, L Interactive Learning of Pattern Rankings. International Journal on Artificial Intelligence Tools vol.23(6), World Scientific, 2014.
	Blockeel, H, van Leeuwen, M & Vinciotti, V (eds) Advances in Intelligent Data Analysis XIII (proceedings of IDA 2014). LNCS 8819, Springer, 2014.
	van Leeuwen, M & Vreeken, J Mining and Using Sets of Patterns through Compression. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 165-198, Springer, 2014.
	van Leeuwen, M & Ukkonen, A Estimating the pattern frequency spectrum inside the browser. Technical Report arXiv:1409.7311, arXiv, 2014.
	van Leeuwen, M & Ukkonen, A Fast Estimation of the Pattern Frequency Spectrum. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'14), pp 114-129, Springer, 2014.
	Le Van, T, van Leeuwen, M, Nijssen, S, Fierro, AC, Marchal, K & De Raedt, L Ranked Tiling. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'14), pp 98-113, Springer, 2014.
	Siebes, A, van Leeuwen, M & Vreeken, J Information Theoretic Methods in Data Mining. At the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'14), Nancy, France, 2014.
	Chau, DH, Vreeken, J, van Leeuwen, M & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'14). 2014.
	Pool, S, Bonchi, F & van Leeuwen, M Description-driven Community Detection. Transactions on Intelligent Systems and Technology vol.5(2), ACM, 2014.
	van Leeuwen, M Interactive Data Exploration using Pattern Mining. In: Holzinger, A & Jurisica, I (eds) Interactive Knowledge Discovery and Data Mining: State-of-the-Art and Future Challenges in Biomedical Informatics, LNCS 8401, Springer, 2014.

2013

	Dzyuba, V, van Leeuwen, M, Nijssen, S & De Raedt, L Active Preference Learning for Ranking Patterns. In: Proceedings of the International Conference on Tools with Artificial Intelligence 2013 (ICTAI'13), IEEE, 2013. (Best Paper)
	Dzyuba, V & van Leeuwen, M Interactive Discovery of Interesting Subgroup Sets. In: Proceedings of the Twelfth International Symposium on Intelligent Data Analysis (IDA 2013), pp 150-161, Springer, 2013.
	van Leeuwen, M & Ukkonen, A Discovering Skylines of Subgroup Sets. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13), pp 272-287, Springer, 2013.
	De Raedt, L, Paramonov, S & van Leeuwen, M Relational Decomposition using Answer Set Programming. In: LNMR'13 - 1st Workshop on Learning and Non-Monotonic Reasoning (at LPNMR), 2013.
	Chau, DH, Vreeken, J, van Leeuwen, M & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'13). ACM, 2013.

2012

	Le Van, T, Fierro, C, Guns, T, van Leeuwen, M, Nijssen, S, De Raedt, L & Marchal, K Mining Local Staircase Patterns in Noisy Data. In: Proceedings of the ICDM 2012 workshops (CoClus workshop), IEEE, 2012.
	van Leeuwen, M & Puspitaningrum, D Improving Tag Recommendation using Few Associations. In: Proceedings of the Eleventh International Symposium on Intelligent Data Analysis Data 2012 (IDA 2012), pp 184-194, Springer, 2012.
	Vreeken, J, van Leeuwen, M, Nijssen, S, Tatti, N, Dries, A & Goethals, B (eds) Proceedings of the ECML PKDD Workshop on Instant Interactive Data Mining (IID'12). 2012.
	van Leeuwen, M & Knobbe, AJ Diverse Subgroup Set Discovery. Data Mining and Knowledge Discovery vol.25(2), pp 208-242, Springer, 2012. (ECMLPKDD'11 Special Issue)

2011

	van Leeuwen, M & Knobbe, AJ Non-Redundant Subgroup Discovery in Large and Complex Data. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data 2011 (ECML PKDD'11), pp 459-474, Springer, 2011.
	Bonchi, F, van Leeuwen, M & Ukkonen, A Characterizing Uncertain Data using Compression. In: Proceedings of the SIAM Conference on Data Mining 2011 (SDM'11), SIAM, 2011.
	Vreeken, J, van Leeuwen, M & Siebes, A Krimp: Mining Itemsets that Compress. Data Mining and Knowledge Discovery vol.23(1), pp 169-214, Springer, 2011.

2010

	Duivesteijn, W, Knobbe, AJ, Feelders, A & van Leeuwen, M Subgroup Discovery meets Bayesian networks – an Exceptional Model Mining approach. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10), IEEE, 2010.
	van Leeuwen, M Maximal Exceptions with Minimal Descriptions. Data Mining and Knowledge Discovery vol.21(2), pp 259-276, Springer, 2010. (ECMLPKDD'10 Special Issue)
	van Leeuwen, M Patterns that Matter. Dissertation, Universiteit Utrecht, 2010.

2009

	van Leeuwen, M, Bonchi, F, Sigurbjörnsson, B & Siebes, A Compressing Tags to Find Interesting Media Groups. In: Proceedings of the ACM Conference on Information and Knowledge Management (CIKM'09), pp 1147-1156, ACM, 2009. (Runner-up Best Student Paper)
	van Leeuwen, M, Vreeken, J & Siebes, A Identifying the Components. Data Mining and Knowledge Discovery vol.19(2), pp 176-193, Springer, 2009. (ECMLPKDD'09 Special Issue) (Best Student Paper)
	van Leeuwen, M, Vreeken, J & Siebes, A Identifying the Components. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'09), pp 32-32, Springer, 2009. (ECMLPKDD'09 Best Student Paper)

2008

van Leeuwen, M & Siebes, A StreamKrimp: Detecting Change in Data Streams. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data (ECMLPKDD'08), pp 672-687, LNCS 5211, Springer, 2008.

2007

	Vreeken, J, van Leeuwen, M & Siebes, A Preserving Privacy through Data Generation. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'07), pp 685-690, IEEE, 2007.
	Siebes, A, van Leeuwen, M & Vreeken, J MDL for Pattern Mining. In: Proceedings of the International Conference on Statistics for Data Mining, Learning and Knowledge Extraction Models (IASC'07), 2007.
	Vreeken, J, van Leeuwen, M & Siebes, A Characterising the Difference. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2007 (KDD'07), pp 765-774, ACM, 2007.
	Vreeken, J, van Leeuwen, M & Siebes, A Privacy Preservation through Data Generation. Technical Report UU-CS-2007-020, Universiteit Utrecht, 2007.

2006

	van Leeuwen, M, Vreeken, J & Siebes, A Compression Picks the Item Sets that Matter. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'06), pp 585-592, Springer, 2006.
	Siebes, A, Vreeken, J & van Leeuwen, M Item Sets That Compress. In: Proceedings of the SIAM International Conference on Data Mining (SDM'06), pp 393-404, SIAM, 2006.
	van Leeuwen, M, Vreeken, J & Siebes, A Compression Picks the Significant Item Sets. Technical Report UU-CS-2006-050, Universiteit Utrecht, 2006.

2004

van Leeuwen, M Spike Timing Dependent Structural Plasticity, in a single model neuron. M.Sc. Thesis, Universiteit Utrecht, 2004.

2003

Koopman, ACM, van Leeuwen, M & Vreeken, J Exploring Temporal Memory of LSTM and Spiking Circuits. In: Workshop on the Future of Neural Networks (FUNN'03), 2003.

2002

	van Leeuwen, M Evolutionary blimp & i. Technical Report (internship), Universiteit Utrecht, 2002.
	Zufferey, J-C, Floreano, D, van Leeuwen, M & Merenda, T Evolving Vision-based Flying Robots. In: Proceedings of the 2nd International Workshop on Biologically Motivated Computer Vision 2002 (BMCV'02), 2002.