Selected Publications

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

  • Steffen Bickel, Michael Brückner, and Tobias Scheffer.
    Discriminative learning for differing training and test distributions.
    Proceedings of the International Conference on Machine Learning, 2007.
  • Laura Dietz, Steffen Bickel, and Tobias Scheffer.
    Unsupervised prediction of citation influences.
    Proceedings of the International Conference on Machine Learning, 2007.
  • Peter Haider, Ulf Brefeld, and Tobias Scheffer.
    Supervised clustering of streaming data for email batch detection.
    Proceedings of the International Conference on Machine Learning, 2007. Best Student Paper Award.
  • Alexander Zien, Ulf Brefeld, and Tobias Scheffer.
    Transductive Support Vector Machines for Structured Variables.
    Proceedings of the International Conference on Machine Learning, 2007.
  • David Vogel, Ognian Asparouhov, and Tobias Scheffer.
    Scalable look-ahead linear regression trees.
    Proceedings of the SIGKDD Conference of Knowledge Discovery and Data Mining, 2007.
  • Steffen Bickel, Peter Haider, Tobias Scheffer, Rene Wienholtz.
    A computer implemented system and a method for detecting abuse of an electronic mail infrastructure in a computer network.
    European Patent Application EP07004097, 2007.
  • Peter Haider, Arne Jansen, and Tobias Scheffer.
    A method of filtering electronic mail and an electronic mail system.
    European Patent Application EP07004098, 2007.
  • Michael Brückner, Peter Haider, and Tobias Scheffer.
    Highly scalable discriminative spam filtering.
    Proceedings of the Text Retrieval Conference (TREC), 2007.

2006

2005

2004

2003

2002

2001

2000

1999

1998

  • Tobias Scheffer and Thorsten Joachims.
    Estimating the expected error of empirical minimizers for model selection. Abstract.  Pre-print of full paper.
    in Proceedings of the National Conference on Artificial Intelligence (AAAI), 19998.

1997

  • Tobias Scheffer and Ralf Herbrich.
    Unbiased Assessment of Learning Algorithms.
    Proceedings of the International Joint Conference on Artificial Intelligence . Nagoya, Japan, 1997.
  • Tobias Scheffer, Russel Greiner, and Christian Darken.
    Why experimentation can be better than perfect guidance.
    Proceedings of the International Conference on Machine Learning . Nashville, TN, 1997.
  • Ralf Herbrich and Tobias Scheffer.
    Generation of task-specific segmentation procedures as a model selection task.
    Proceedings of the Visual Information Processing Workshop. Sydney, 1997.

1996

  • T. Scheffer, R. Herbrich, F. Wysotzki.
    Efficient theta-subsumption based on graph algorithms. ( revised version )
    Muggleton, editor, Inductive Logic Programming, 6th International Workshop, Selected Papers, LNAI 1314, pp. 212-228, Springer Verlag Berlin, 1996
  • T. Scheffer, R. Herbrich, F. Wysotzki.
    Efficient theta-subsumption based on graph algorithms.
    Proceedings of the International Workshop on Inductive Logic Programming . Stockholm, Sweden, 1996.
  • T. Scheffer, R. Herbrich, F. Wysotzki.
    Graph based subsumption algorithms for machine learning.
    Beiträge zum Fachgruppentreffen Maschinelles Lernen. Chemnitz, 1996.
  • M. Finke, G. Hommel, T. Scheffer and F. Wysotzki.
    Aerial robotics in computer science education.
    Computer Science Education. 7(2): 239-246, 1996.
  • Linda Briesemeister, Tobias Scheffer, and Fritz Wysotzki.
    A concept-formation based algorithmic model for skill-acquisition.
    Cognitive Modelling, 1996.
  • Tobias Scheffer.
    Algebraic foundation and improved methods of induction of ripple down rules.
    Proceedings of the Pacific Rim Workshop on Knowledge Acquisition. Sydney, Australia, 1996.

1995

  • Tobias Scheffer.
    Learning Rules with Nested Exceptions.
    Proceedings International Workshop on Artificial Intelligence Techniques , Brno, Czech Republic, 1995.
  • Tobias Scheffer
    Induktion Hierarchischer Regelsysteme.
    Master's Thesis, Technische Universität Berlin. 1995.
  • T. Scheffer.
    A Generic Algorithm for Learning Rules with Hierarchical Exceptions (extended abstract).
    KI-95 - Advances in Artificial Intelligence , Springer. Saarbrücken, 1995.

1994

  • Marek Musial, Tobias Scheffer.
    A Term-Based Genetic Code for ANNs.
    KI-94 Extended Abstracts, Springer-Verlag, Berlin etc, 1994.
  • Marek Musial, Tobias Scheffer.
    A Term-based genetic Code for Artificial Neural Networks.
    Genetic Algorithms within the Framework of Neural Computation, Procceedings of the KI-94 Workschop, Max-Planck-Institut für Informatik, Saarbrücken, 1994


(My Erdös number is at most 4 because Frank Stephan's Erdös number is 3 and we have co-authored a paper.)