Machine Learning Research Group

Niels Landwehr University of Potsdam Department of Computer Science Building 4, Office 0.20 August-Bebel-Str. 89 14482 Potsdam, Germany Phone: +49 331 977 3140 Fax: +49 331 977 3022

Research Interests

• Machine learning and its applications
• Transfer learning
• Learning from dependent and structured data
• Structured output prediction
• Active learning and active evaluation

Publications

• Elisa Cilia, Niels Landwehr, and Andrea Passerini. Relational Feature Mining with Hierarchical Multitask kFOIL. Fundamenta Informaticae 113: 1-26, 2011.
  
  
• Ingo Thon, Niels Landwehr, and Luc De Raedt. Stochastic Relational Processes: Efficient Inference and Applications. Machine Learning 82 (2): 239-277, 2011.
  
  
• Christoph Sawade, Niels Landwehr, and Tobias Scheffer. Active Estimation of F-measures (with Online Appendix). Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS-2010), Vancouver, Canada, 2010.
  
  
• Christoph Sawade, Niels Landwehr, Steffen Bickel, and Tobias Scheffer. Active Risk Estimation. Proceedings of the 27th International Conference on Machine Learning (ICML-2010), Haifa, Israel, 2010.
  
  
• Niels Landwehr. Trading Expressivity for Efficiency in Statistical Relational Learning (Ph.D. Thesis Abstract). SIGKDD Explorations 11(2): 59-60, 2010.
  
  
• Niels Landwehr, Andrea Passerini, Luc De Raedt, and Paolo Frasconi. Fast learning of relational kernels. Machine Learning 78(3): 305-342, 2010.
  
  
• Ingo Thon, Bernd Gutmann, Martijn van Otterlo, Niels Landwehr, and Luc De Raedt. From non-deterministic to probabilistic planning with the help of statistical relational learning. Proceedings of the Workshop on Planning and Learning, in conjunction with the 19th International Conference on Automated Planning and Scheduling (ICAPS-2009), Thessaloniki, Greece, 2009.
  
  
• Elisa Cilia, Niels Landwehr, and Andrea Passerini. Mining Drug Resistance Relational Features with Hierarchical Multitask kFOIL.Proceedings of the Bio-Logical (Logic-based approaches in Bioinformatics) Workshop, Reggio Emilia, Italy, 2009.
  
  
• Laura Antanas, Ingo Thon, Martijn van Otterlo, Niels Landwehr, and Luc De Raedt. Probabilistic Logical Sequence Learning for Video. Proceedings of the 19th Internaitonal Conference on Inductive Logic Programming (ILP-2009), Leuven, Belgium, July 2009.
  
  
• Niels Landwehr. Trading Expressivity for Efficiency in Statistical Relational Learning. Ph.D. Thesis, Katholieke Universiteit Leuven, February 2009.
  
  
• Luc De Raedt, Bart Demoen, Daan Fierens, Bernd Gutmann, Gerda Janssens, Angelika Kimmig, Niels Landwehr, Theofrastos Mantadelis, Wannes Meert, Ricardo Rocha, Vitor Santos Costa, Ingo Thon, Joost Vennekens. Towards Digesting the Alphabet-Soup of Statistical Relational Learning. In Proceedings of the 1st Workshop on Probabilistic Programming: Universal Languages, Systems and Applications, at the 22nd Annual Conference on Neural Information Processing Systems (NIPS-2008).
  
  
• Andreas Karwath, Kristian Kersting, and Niels Landwehr. Boosting Relational Sequence Alignments . In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008) Pisa, Italy, December 2008.
  
  
• Ingo Thon, Niels Landwehr, and Luc De Raedt. A Simple Model for Sequences of Relational State Descriptions. In Proceedings of the 25th European Conference on Machine Learning (ECML-2008), Antwerp, Belgium, September 2008.
  
  
• Niels Landwehr. Modeling Interleaved Hidden Processes. In Proceedings of the 25th International Conference on Machine Learning (ICML-2008), Helsinki, Finland, July 2008.
  
  
• Niels Landwehr, Bernd Gutmann, Ingo Thon, Matthai Philipose, and Luc De Raedt. Relational Transformation-based Tagging for Activity Recognition. In Fundamenta Informaticae 89 (1): 1-19, 2008.
  
  
• Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, and Niels Landwehr. Relational Sequence Learning. In Luc De Raedt, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton (Eds.): Probabilistic Inductive Logic Programming. Springer, 2008.
  
  
• Niels Landwehr and Taneli Mielikäinen. Probabilistic Logic Learning from Haplotype Data. In Luc De Raedt, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton (Eds.): Probabilistic Inductive Logic Programming. Springer, 2008.
  
  
• Niels Landwehr, Bernd Gutmann, Ingo Thon, Matthai Philipose, and Luc De Raedt. Relational Transformation-based Tagging for Human Activity Recognition. In D. Malerba, A. Appice, and M. Ceci (Eds.): Working Notes of the 6th Workshop on Multi-Relational Data Mining at ECML/PKDD-2007, Warsaw, Poland, September 2007.
  
  
• Matti Kääriänen, Niels Landwehr, Sampsa Lappalainen and Taneli Mielikäinen. Combining Haplotypers. CoRR abs/0710.5116, Technical Report C-2007-57, University of Helsinki, Department of Computer Science, 2007.
  
  
• Niels Landwehr, Kristian Kersting, and Luc De Raedt. Integrating Naive Bayes and FOIL. In Journal of Machine Learning Research 8 (1) 481-507, 2007.
  
  
• Niels Landwehr, Taneli Mielikinen, Lauri Eronen, Hannu Toivonen, Heikki Mannila. Constrained Hidden Markov Models for Population-based Haplotyping. In BMC Bioinformatics 8 (Suppl 2): S9. 2007.
  
  
• Niels Landwehr and Luc De Raedt. r-grams: Relational Grams . In Proceedings of the Twentieth Joint International Conference on Artificial Intelligence (IJCAI-2007), Hyderabad, India, January 2007.
  
  
• Niels Landwehr, Andrea Passerini, Luc De Raedt, and Paolo Frasconi. kFOIL: Learning Simple Relational Kernels. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-2006), Boston, MA, USA, July 2006.
  
  
• Niels Landwehr, Taneli Mielikainen, Lauri Eronen, Hannu Toivonen, and Heikki Mannila. Constrained Hidden Markov Models for Population-based Haplotyping (Extended Abstract). In Juho Rouso, Samuel Kaski, and Esko Ukkonen (Eds.): Proceedings of the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB), Helsinki, Finland, June 2006.
  
  
• Niels Landwehr, Kristian Kersting, and Luc De Raedt. nFOIL: Integrating Naive Bayes and FOIL. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-2005), Pittsburgh, PA, USA, July 2005.
  
  
• Niels Landwehr, Mark Hall, and Eibe Frank. Logistic Model Trees . In Machine Learning 59 (1-2) 161-205, 2005.
  
  
• Niels Landwehr, Mark Hall, and Eibe Frank. Logistic Model Trees. In Proceedings of the 14th European Conference on Machine Learning (ECML-2003), Cavtat (Dubrovnik), Croatia, September 2003.
  
  
• Niels Landwehr. Logistic Model Trees. Diplomarbeit, Albert-Ludwigs-Universität Freiburg, Institut für Informatik, July 2003.
  
  
• Kristian Kersting and Niels Landwehr. Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm . In J. A. Gámez, S. Moral, and A. Salmerón (Eds.): Advances in Learning Bayesian Networks. Studies in Fuzziness and Soft Computing 146, 2002.
  
  

Brief Bio

I obtained a M.Sc. in computer science ("Diplom Informatik", scl) from the Albert-Ludwigs-Universität Freiburg, Germany, in July 2003; and a Ph.D. in machine learning ("Doctoraat", scl) from the Katholieke Universiteit Leuven, Belgium, in February 2009. Since February 2009 I am a post-doctoral researcher at the University of Potsdam, Germany.

In 2010, I was awarded the Scientific Prize IBM Belgium for Informatics (annual price for the best Ph.D. thesis in computer science in Belgium) and the ECCAI Artificial Intelligence Dissertation Award (annual price for the best Ph.D. thesis in Artificial Intelligence in Europe).

Programme Committee and Editorial Board Service

• AAAI, Conference of the Association for Advancement of Artificial Intelligence ['10, '12]

• AISTATS, International Conference on Artificial Intelligence and Statistics ['09]

• ECML, European Conference on Machine Learning ['07, '09]

• ECAI, European Conference on Artificial Intelligence ['12]

• ICML, International Conference on Machine Learning ['09, '10], '11, '12]

• IJCAI, International Joint Conference on Artificial Intelligence ['09, '11]

• ILP, International Conference on Inductive Logic Programming ['09, '10]

• NIPS, Neural Information Processing Systems (reviewer) ['08, '09]

• SRL, Workshop on Statistical Relational Learning ['09]

I am a member of the editorial board of the Machine Learning Journal.

Software

• "LMT" - an implementation of the Logistic Model Tree classifier. Part of the WEKA machine learning toolbox.

• "Spamm" - a statistical method for population-based haplotyping. Download link.

• "kFOIL" - a statistical relational learning system combining ILP and kernels. Download link.

• "nFOIL" - a statistical relational learning system combining ILP and naive Bayes. Part of the PROFILE toolbox for probabilistic first-order learning.

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