Girschick, T, Rückert, U, and Kramer, S (accepted).
Adapted Transfer of Distance Measures for Quantitative Structure-Activity Relationships and Data-Driven Selection of Source Datasets
The Computer Journal.

Rückert, U, Girschick, T, Buchwald, F, and Kramer, S (2010).
Adapted Transfer of Distance Measures for Quantitative Structure-Activity Relationships
In: Proceedings of the 13th International Conference on Discovery Science, ed. by B. Pfahringer, G. Holmes, A. Hoffman, vol. 6332, pp. 341-355, Springer. LNCS/LNAI.

Rückert, U (2008).
A Statistical Approach to Rule Learning
PhD Thesis, Technische Universität München.

Rückert, U and De Raedt, L (2008).
An Experimental Evaluation of Simplicity in Rule Learning
Artificial Intelligence, 172(1):19-28.

Rückert, U and Kramer, S (2008).
Kernel-Based Inductive Transfer
In: Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II, ed. by Walter Daelemans, Bart Goethals, Katharina Morik, pp. 220-233, Springer. Lecture Notes in Computer Science.

Rückert, U and Kramer, S (2007).
Optimizing Feature Sets for Structured Data
In: Machine Learning: ECML 2007, 18th European Conference on Machine Learning, ed. by Joost N. Kok, Jacek Koronacki, Ramon Lopez de Mantaras, Stan Matwin, Dunja Mladenic, Andrzej Skowron, pp. 716-723, Berlin, Springer. Lecture Notes in Computer Science Vol. 4701.

Rückert, U and Kramer, S (2007).
Margin-Based First-Order Rule Learning
Machine Learning, 70(2-3):189-206.

Rückert, U and Kramer, S (2007).
Towards a Framework for Relational Learning and Propositionalization
In: Proceedings of the 6th Workshop on Multi-Relational Data Mining at the 18th European Conference on Machine Learning, ed. by D. Malerba, A. Appice, M. Ceci.

Friedel, C, Rückert, U, and Kramer, S (2006).
Cost Curves for Abstaining Classifiers
In: Proc. of the ICML 2006 workshop on ROC Analysis in Machine Learning, Pittsburgh, PA.

Richter, L, Rückert, U, and Kramer, S (2006).
Learning a Predictive Model for Growth Inhibition from the NCI DTP Human Tumor Cell Line Screening Data: Does Gene Expression Make a Difference?
In: Pacific Symposium on Biocomputing, vol. 11, pp. 596-607.