Selected Publications
2015
2014
2013
- Michael Großhans, Christoph Sawade, Michael Brückner, and Tobias Scheffer.
Bayesian Games for Adversarial Regression Problems (appendix).
Proceedings of the International Conference on Machine Learning, 2013.
- Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, and Niels Landwehr.
Active Evaluation of Ranking Functions based on Graded Relevance.
Machine Learning Journal, 92(1), 41-64, 2013.
- Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, and Niels Landwehr.
Active Evaluation of Ranking Functions based on Graded Relevance (Extended Abstract).
Proceedings of the International Joint Conference on Artificial Intelligence, Invited Track on Best Papers from Sister Conferences, 2013
2012
- Michael Brückner, Christian Kanzow, and Tobias Scheffer.
Static Prediction Games for Adversarial Learning Problems.
Journal of Machine Learning Research 13:2617-2654, 2012
- Christoph Sawade, Niels Landwehr, and Tobias Scheffer.
Active Comparison of Prediction Models (online appendix).
Advances in Neural Information Processing Systems, 2012.
- Paul Prasse, Christoph Sawade, Niels Landwehr, and Tobias Scheffer.
Learning to identify regular expressions that describe email campaigns.
Proceedings of the International Conference on Machine Learning, 2012.
- Peter Haider and Tobias Scheffer.
Finding botnets using minimal graph clusterings.
Proceedings of the International Conference on Machine Learning, 2012.
- Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, and Niels Landwehr.
Active Evaluation of Ranking Functions based on Graded Relevance.
Proceedings of the European Conference on Machine Learning 2012. ECML Best Paper Award.
2011
- Lise Getoor, Tobias Scheffer (Editors).
Proceedings of the 28th International Conference on Machine Learning.
Bellevue, Washington, USA, June 28 - July 2, Omnipress 2011
- Michael Brückner and Tobias Scheffer.
Stackelberg games for adversarial prediction problems.
Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, 2011.
- Michael Brückner, Christian Kanzow, and Tobias Scheffer.
Static Prediction Games for Adversarial Learning Problems.
Preprint 304, Institute of Mathematics, University of Würzburg, August 2011.
- K. R. Patil, P. Haider, P. B. Pope, P. J. Turnbaugh, M. Morrison, T. Scheffer, and A. C. McHardy.
Taxonomic metagenome sequence assignment with structured output models.
Nature Methods, 8(3), 2011.
2010
- Christoph Sawade, Niels Landwehr, Tobias Scheffer.
Active evaluation of F-measures (Online Appendix).
Advances in Neural Information Processing Systems, 2010.
- Uwe Dick, Peter Haider, Tobias Scheffer.
Throttling Poisson processes.
Advances in Neural Information Processing Systems, 2010.
- Christoph Sawade, Niels Landwehr, Steffen Bickel, and Tobias Scheffer.
Active Risk Estimation.
Proceedings of the International Conference on Machine Learning, 2010.
2009
- Michael Brückner and Tobias Scheffer.
Nash equilibria of static prediction games (with appendix).
Advances in Neural Information Processing Systems, 2009.
- Laura Dietz, Valentin Dallmeier, Andreas Zeller, and Tobias Scheffer.
Localizing bugs in program executions with graphical models.
Advances in Neural Information Processing Systems, 2009.
- Peter Haider and Tobias Scheffer.
Bayesian clustering for email batch detection.
Proceedings of the International Conference on Machine Learning, 2009.
- Steffen Bickel, Michael Brückner, and Tobias Scheffer.
Discriminative learning under covariate shift.
Journal of Machine Learning Research 10: 2137-2155, 2009.
- Szymon Jaroszewicz, Tobias Scheffer, and Dan A. Simovici.
Scalable pattern mining with Bayesian networks as background knowledge.
Data Mining and Knowledge Discovery 18:56-100, 2009.
2008
- Steffen Bickel, Christoph Sawade, and Tobias Scheffer.
Transfer Learning by Distribution Matching for Targeted Advertising.
Advances in Neural Information Processing Systems, 2008.
- Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer, and Tobias Scheffer.
Multi-task learning for HIV therapy screening.
Proceedings of the International Conference on Machine Learning, 2008.
- Uwe Dick, Peter Haider, and Tobias Scheffer.
Learning from incomplete data with infinite imputations.
Proceedings of the International Conference on Machine Learning, 2008.
- Szymon Jaroszewicz, Lenka Ivantysynova, and Tobias Scheffer.
Schema matching on streams with accuracy guarantees.
Intelligent Data Analysis 12: 253-270, 2008.
- Thoralf Klein, Ulf Brefeld, and Tobias Scheffer.
Exact and approximate inference for annotating graphs with structural SVMs.
Proceedings of the European Conference on Machine Learning, 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
- Steffen Bickel and Tobias Scheffer.
Dirichlet-Enhanced Spam Filtering based on Biased Samples.
Advances in Neural information Processing Systems, 2006.
- Peter Haider, Ulf Brefeld, and Tobias Scheffer.
Discriminative Identification of Duplicates.
Proceedings of the ECML Workshop on Mining and Learning in Graphs, 2006.
- Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, and Stefan Wrobel.
Efficient co-regularized least squares regression.
Proceedings of the International Conference on Machine Learning. 2006.
- Ulf Brefeld and Tobias Scheffer.
Semi-supervised learning for structured output variables.
Proceedings of the International Conference on Machine Learning. 2006
- Tobias Scheffer and Stefan Wrobel.
Finding the most interesting patterns in a database quickly by using sequential sampling.
European Patent EP1 346 293; PCT/EP2001/009541. Bulletin 2006/26.
- Szymon Jaroszewicz, Lenka Ivantysynova, and Tobias Scheffer.
Accurate Schema Matching on Streams. (full version with proof)
Proceedings of the ECML/PKDD Workshop on Knowledge Discovers from Streams. 2006.
- Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou, editors.
Proceedings of the European Conference on Machine Learning.
Springer LNCS 4212, 2006.
- Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou, editors.
Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases.
Springer LNCS 4213, 2006.
2005
- David Vogel, Steffen Bickel, Peter Haider, Rolf Schimpfky, Peter Siemen, Steve Bridges, Tobias Scheffer.
Classifying search engine queries using the web as background knowledge.
SIGKDD Explorations 7(2): 117-122. 2005.
- Szymon Jaroszewicz and Tobias Scheffer.
Fast Discovery of Unexpected Patterns in
Data, Relative to a Bayesian
Network.
Proceedings of the SIGKDD
International Conference on Knowledge Discovery and Data Mining.
2005.
-
Ulf Brefeld, Christoph Büscher, and Tobias Scheffer.
Multi-view discriminative sequential
learning.
Proceedings of the European Conference on Machine Learning. 2005. Best Paper Award.
- Steffen Bickel, Peter Haider, and Tobias Scheffer.
Predicting sentences using N-gram
language models.
Proceedings of the Conference on
Empirical Methods in Natural Language Processing, 2005.
- Steffen Bickel and Tobias Scheffer.
Estimation of mixture models using Co-EM.
Proceedings of the European Conference on Machine Learning. 2005.
A longer version that includes the
proof of Theorem 1 appeared in the
Proceedings of the ICML Workshop
on Learning with Multiple Views. 2005.
- Steffen Bickel, Peter Haider, and Tobias Scheffer.
Learning to complete sentences.
Proceedings of the European
Conference on Machine Learning. 2005.
- Isabel Drost and Tobias Scheffer.
Thwarting the nigritude
ultramarine: learning to identify link spam.
Proceedings of the European Conference on Machine Learning. 2005.
- Isabel Drost, Steffen Bickel, and Tobias Scheffer.
Discovering Communities in
Linked Data by Multi-View Clustering.
Proceedings of the Conference
of the German Classification Society, 2005.
- U. Brefeld, C. Büscher, and T. Scheffer.
Multi-View
Hidden Markov Perceptrons.
Proceedings of the German
Workshop on Machine Learning (FGML), 2005.
- Ulf Brefeld and Tobias Scheffer.
AUC
Maximizing Support Vector Learning.
Proceedings of the ICML 2005
Workshop on ROC Analysis in Machine
Learning. 2005.
- Jörg Hakenberg, Steffen Bickel, Conrad Plake, Ulf Brefeld,
Hagen Zahn, Lukas Faulstich, Ulf Leser, and Tobias Scheffer.
Systematic
feature evaluation for gene name recognition.
BMC
Bioinformatics 6(1), 2005.
- Tobias Scheffer.
Finding association rules that trade support
optimally against confidence. Draft.
Intelligent
Data Analysis 9(3), 2005.
- Stefan Rüping and Tobias Scheffer, editors.
Proceedings of the ICML Workshop on
Learning with Multiple Views, 2005.
- Achim Hoffmann, Hiroshi Motoda, and Tobias Scheffer, editors.
Proceedings of the International Conference on Discovery Science. Springer LNAI 3735. 2005.
2004
- Steffen Bickel and Tobias Scheffer.
Multi-view
clustering.
Proceedings of the
IEEE International Conference on Data Mining. 2004.
- Ulf Brefeld and Tobias Scheffer.
Co-EM
Support Vector Learning.
Proceedings
of the International
Conference on Machine Learning. 2004.
- Korinna Grabski and Tobias Scheffer.
Sentence
Completion.
Proceedings of the
SIGIR International
Conference on Information Retrieval. 2004.
- Steffen Bickel and Tobias
Scheffer.
Learning
from Message Pairs for Automatic Email Answering.
Proceedings of the European Conference on
Machine Learning, 2004.
- Tobias Scheffer.
Email answering assistance by semi-supervised
text classification. Draft.
Intelligent
Data Analysis, 8(5), 2004.
- Mark-A. Krogel and Tobias Scheffer.
Multirelational
learning,
text mining, and semi-supervised learning for functional genomics.
Machine Learning 57(1/2):61-81,
2004.
- Tobias Scheffer, editor.
Proceedings of the Second European
Workshop on Data Mining and Text Mining for Bioinformatics. 2004.
- Steffen
Bickel, Ulf Brefeld, Lukas Faulstich, Jörg Hakenberg, Ulf Leser,
Condrad Plake, and Tobias Scheffer.
A
Support Vector Machine classifier for gene name recognition.
EMBO
Workshop: A Critical Assessment of Text Mining Methods in Molecular
Biology. Granada, Spain, March 2004.
2003
- Mark-A. Krogel and Tobias Scheffer.
Effectiveness
of information extraction, multi-relational and semi-supervised
learning
for predicting functional properties of genes .
Proceedings
of the IEEE International Conference on Data Mining. 2003.
- Tobias Scheffer and Ulf Leser, editors.
Proceedings
of the European Workshop on Data Mining and Text Mining for
Bioinformatics . 2003.
- Mark-A. Krogel and Tobias Scheffer.
Effectiveness
of information extraction, multi-relational and multi-view learning for
predicting gene deletion experiments.
Proceedings of the
Third ACM SIGKDD International Workshop
on Data Mining for Bioinformatics. 2003.
- Mark-A. Krogel, Marcus Denecke, Marko Landwehr and Tobias
Scheffer.
Using Data and Text Mining Techniques for Yeast Gene Regulation
Prediction: A Case Study.
SIGKDD Explorations 4(2),
2003.
- Michael Kockelkorn, Andreas Lüneburg, and Tobias Scheffer.
Using transduction and multi-view learning to
answer emails.
Proceedings of the European Conference on
Principles and Practice of Knowledge Discovery in Databases. 2003.
- Michael Kockelkorn, Andreas Lüneburg, and Tobias Scheffer
Learning to answer emails.
Proceedings
of the International Symposium on Intelligent Data Analysis . 2003.
2002
- Tobias Scheffer and Stefan Wrobel.
Finding the Most Interesting
Patterns in a Database Quickly by Using Sequential Sampling.
PDF ,
Gzipped Postscript ,
Postscript.
Journal
of Machine Learning Research 3:833-862. 2002.
- Tobias Scheffer and Stefan Wrobel.
A
scalable constant-memory sampling algorithm for pattern discovery in
large databases.
Proceedings of the European Conference on
Principles and Practice of Knowledge Discovery and Data Mining.
2002.
- Tobias Scheffer and Stefan Wrobel.
Text classification beyond the bag-of-words representation
Proceedings
of the ICML-Workshop on Text Learning. 2002.
- Tobias Scheffer, Stefan Wrobel, Borislav Popov, Damyan Ognianov,
Christian Decomain, and Susanne Hoche.
Learning hidden Markov models for information extraction actively from
partially labeled text.
Künstliche Intelligenz.
2/2002.
- M.-A. Krogel, M. Denecke, M. Landwehr, and T. Scheffer.
Using Data and Text Mining Techniques for Yeast Gene Regulation
Prediction: A Case Study.
Beiträge zum
GI-Fachgruppentreffen Maschinelles Lernen (FGML) . 2002.
2001
- Tobias Scheffer, Chistian Decomain, and Stefan Wrobel.
Mining the web with active hidden Markov models
Proceedings
of the IEEE International Conference on Data Mining . 2001.
- Hans Gründel, Tino Naphtali, Christian Wiech, Jan-Marian
Gluba, Maiken Rohdenburg, and Tobias Scheffer.
Clipping and analyzing news using machine learning techniques.
Proceedings
of the International Conference on Discovery Science . 2001.
- Tobias Scheffer and Stefan Wrobel.
Active learning of partially hidden Markov models.
Active
Learning, Database Sampling, Experimental Design: Views on Instance
Selection.. 2001.
- Tobias Scheffer, Christian Decomain, and Stefan Wrobel.
Active hidden Markov models for information extraction.
Proceedings
of the International Symposium on Intelligent Data Analysis . 2001.
- Tobias Scheffer and Stefan Wrobel (Editors).
Active Learning, Database Sampling, Experimental Design: Views on
Instance Selection. Proceedings of the ECML/PKDD Workshop .
2001.
- Tobias Scheffer and Stefan Wrobel.
Incremental maximization of non-instance-averaging utility functions
with applications to knowledge discovery problems.
Proceedings
of the International Conference on Machine Learning . Williams
College, MA, 2001.
- Tobias Scheffer.
Finding association rules that trade support
optimally against
confidence.
Proceedings of the European Conference on
Principles and Practice of Knowledge Discovery in Databases (PKDD-01).
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
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.)