Selected Publications

2024

• Pedro A. Campana, Paul Prasse, Tobias Scheffer.
  Predicting dose-response curves with deep neural networks.
  Proceedings of the International Conference on Machine Learning, PMLR 235, 2024.
• Pedro A. Campana, Paul Prasse, Matthias Lienhard, Kristina Thedinga, Ralf Herwig, Tobias Scheffer.
  Cancer drug sensitivity estimation using modular deep graph neural networks.
  NAR Genomics and Bioinformatics 6(2), doi.org/10.1093/nargab/lqae043, 2024.
• Paul Prasse, David R. Reich, Silvia Makowski, Tobias Scheffer, Lena A. Jäger.
  Improving cognitive-state analysis from eye gaze with synthetic eye-movement data.
  Computers & Graphics, 119, doi.org/10.1016/j.cag.2024.103901, 2024.

2023

• Shuwen Deng, Paul Prasse, David R. Reich, Tobias Scheffer, Lena A. Jäger.
  Pre-trained language models augmented with synthetic scanpaths for natural language understanding.
  Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023.
• Silvia Makowski, Paul Prasse, Lena A. Jäger, Tobias Scheffer.
  Detection of drowsiness and impending microsleep from eye movements.
  Proceedings of the NeurIPS Workshop Gaze Meets ML, 2023.
• Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller, Tobias Scheffer and Lena A. Jäger.
  Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading.
  Proceedings of the ACM CHI Conference on Human Factors in Computing Systems, 2023. 
• Silvia Makowski, Annika Bätz, Paul Prasse, Lena A. Jäger, Tobias Scheffer.
  Detection of Alcohol Inebriation from Eye Movements.
  Proceedings of the International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, 2023.
• Daniel Krakowczyk, Paul Prasse, David R. Reich, Sebastian Lapuschkin, Tobias Scheffer and Lena A. Jäger.
  Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models.
  Proceedings of the ACM Symposium on Eye-Tracking Research and Applications, 2023.
• Paul Prasse, David R. Reich, Silvia Makowski, Seoyoung Ahn, Tobias Scheffer and Lena A. Jäger.
  SP-EyeGAN: Generating Synthetic Eye Movement Data with Generative Adversarial Networks.
  Proceedings of the ACM Symposium on Eye-Tracking Research and Applications, 2023.
• Daniel G. Krakowczyk, David R. Reich, Jakob Chwastek, Assunta Süss, Paul Prasse, Deborah N. Jakobi, Oleksii Turuta, Paweł Kasprowski and Lena A. Jäger.
  pymovements: A Python package for eye movement data processing.
  Proceedings of the ACM Symposium on Eye-Tracking Research and Applications, 2023.

2022

• Paul Prasse, Pascal Iversen, Matthias Lienhard, Kristina Thedinga, Ralf Herwig, and Tobias Scheffer.
  Pre-training on in vitro and fine-tuning on patient-derived data improves deep neural networks for anti-cancer drug-sensitivity prediction.
  Cancers, 14(16), 3950, https://doi.org/10.3390/cancers14163950, 2022.
• Silvia Makowski, Paul Prasse, Lena A. Jäger, and Tobias Scheffer.
  Oculomotoric biometric identification under the influence of alcohol and fatigue.
  International Joint Conference on Biometrics, 2022.
• Shuwen Deng, Paul Prasse, David R. Reich,  Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, and Lena A. Jäger. 
  Detection of ADHD based on eye movements during natural viewing.
  Proceedings of the European Conference on Machine Learning (ECML-PKDD), 2022.
• Daniel Krakowczyk, David R. Reich, Paul Prasse, Sebastian Lapuschkin, Tobias Scheffer, and Lena A. Jäger
  Selection of XAI Methods Matters: Evaluation of Feature Attribution Methods for Oculomotoric Biometric Identification.
  NeurIPS Workshop on Gaze Meets ML, 2022.
• Paul Prasse, David R. Reich,  Silvia Makowski, Lena A. Jäger and Tobias Scheffer. 
  Fairness in oculomotoric biometric identification.
  ACM Symposium on Eye-Tracking Research and Applications, 2022.
• David R. Reich, Paul Prasse, Chiara Tschirner, Patrick Haller, Frank Goldhammer and Lena A. Jäger. 
  Inferring native and non-native human reading comprehension and subjective text difficulty from scanpaths in reading.
  ACM Symposium on Eye-Tracking Research and Applications, 2022.
• Paul Prasse, Pascal Iversen, Matthias Lienhard, Kristina Thedinga, Chris Bauer, Ralf Herwig, Tobias Scheffer.
  Matching anticancer compounds and tumor cell lines by neural networks with ranking loss.
  NAR Genomics and Bioinformatics 4(1), https://doi.org/10.1093/nargab/lqab128, 2022.

2021

• Silvia Makowski, Paul Prasse, David R. Reich, Daniel Krakowczyk, Lena A. Jäger, Tobias Scheffer.
  DeepEyedentificationLive: Oculomotoric Biometric Identification and Presentation-Attack Detection using Deep Neural Networks.
  IEEE Transactions on Biometrics, Behavior, and Identity Science, DOI: 10.1109/TBIOM.2021.3116875, 2021.
• Paul Prasse, Jan Brabec, Jan Kohout, Martin Kopp,  Lukas Bajer, Tobias Scheffer. 
  Learning Explainable Representations of Malware Behavior (Online Appendix).
  Proceedings of the European Conference on Machine Learning (ECML-PKDD), 2021.
• Chris Bauer, Ralf Herwig, Matthias Lienhard, Paul Prasse, Tobias Scheffer, Johannes Schuchhardt.
  Large‑scale literature mining to assess the relation between anti‑cancer drugs and cancer types.
  Journal of Translational Medicine, 19:274, 2021.

2020

• Silvia Makowski, Lena A. Jäger, Paul Prasse, Tobias Scheffer.
  Biometric identification and presentation-attack detection using micro- and macro-movements of the eyes.
  International Joint Conference on Biometrics, 2020.
• Paul Prasse, Lena A. Jäger, Silvia Makowski, Moritz Feuerpfeil, Tobias Scheffer.
  On the Relationship between Eye Tracking Resolution and Performance of Oculomotoric Biometric Identification. (Online talk)
  International Conference on Knowledge-Based and Intelligent Information & Engineering Systems: Special Session on Eye Movement Data Processing and Analysis, 2020.
• Silvia Makowski, Lena A. Jäger, Lisa Schwetlick, Hans Trukenbrod, Ralf Engbert, Tobias Scheffer. 
  Discriminative Viewer Identification using Generative Models of Eye Gaze. (Online talk)
  International Conference on Knowledge-Based and Intelligent Information & Engineering Systems: Special Session on Eye Movement Data Processing and Analysis, 2020.
• Georgy Ayzel, Tobias Scheffer, Maik Heistermann.
  RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting. 
  Geosci. Model Dev., 13, 2631–2644, 2020.
• Jakub Vrábel, Erik Képeš, Ludovic Duponchel, Vincent Motto-Ros, Cécile Fabre, Sven Connemann, Frederik Schreckenberg, Paul Prasse, Daniel Riebe, Rajendhar Junjuri, Manoj Kumar Gundawar, Xiaofeng Tan, Pavel Pořízka, Jozef Kaiser.
  Classification of challenging Laser-Induced Breakdown Spectroscopy soil sample data - EMSLIBS contest.
  Spectrochimica Acta Part B: Atomic Spectroscopy, volume 169, July 2020, 105872.
• Hanna Drimalla, Tobias Scheffer, Niels Landwehr, Irina Baskow, Stefan Roepke, Behnoush Behnia, Isabel Dziobek.
  Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT).
  Nature Digital Medicine 2(25), 2020.

2019

• Paul Prasse, Rene Knaebel, Lukas Machlika, Tomas Pevny, Tobias Scheffer.
  Joint Detection of Malicious Domains and Infected Clients.
  Machine Learning 108, 1353–1368, 2019.
• Lena A. Jäger, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler, Tobias Scheffer.
  Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye.
  Proceedings of the European Conference on Machine Learning, 2019.

2018

• Silvia Makowski, Lena Jäger, Ahmed Abdelwahab, Niels Landwehr, Tobias Scheffer.
  A discriminative model for identifying readers and assessing text comprehension from eye movements.
  Proceedings of the European Conference on Machine Learning, 2018.
• Hanna Drimalla, Niels Landwehr, Irina Baskow, Behnoush Behnia, Stefan Roepke, Isabel Dziobek, Tobias Scheffer.
  Detecting autism by analyzing a simulated social interaction.
  Proceedings of the European Conference on Machine Learning, 2018.

2017

• Matthias Bussas, Christoph Sawade, Nicolas Kühn, Tobias Scheffer, Niels Landwehr. 
  Varying-coefficient models for geospatial transfer learning. 
  Machine Learning 106(9-19: 1419-1440, doi:10.1007/s10994-017-5639-3, 2017. 
• Paul Prasse, Lukas Machlika, Tomas Pevny, Jiri Havelka, Tobias Scheffer.
  Malware detection by analysing encrypted network traffic with neural networks.
  Proceedings of the European Conference on Machine Learning. 2017.

2016

• Niels Landwehr, Nicolas Kühn, Tobias Scheffer, and Norman Abrahamson. 
  A non-ergodic ground-motion model for California with spatially varying coefficients. 
  Bulletin of the Seismological Society of America  106(6):2574-2583, 2016.  
• Oleksandr Zadorozhnyi, Gunthard Benecke, Stephan Mandt, Tobias Scheffer, Marius Kloft.
  Huber-norm regularization for linear prediction models.
  Proceedings of the European Conference on Machine Learning, 2016.
• Uwe Dick and Tobias Scheffer. Learning to control a structured-prediction decoder for detection of HTTP-layer DDoS Attacks.
  Machine Learning 106 (2-3): 385-410, 2016.

2015

• Paul Prasse, Christoph Sawade, Niels Landwehr, Tobias Scheffer.
  Learning to identify concise regular expressions that describe email campaigns.
  Journal of Machine learning Research 16: 3687-3720, 2015.
• Michael Großhans, Tobias Scheffer.
  Solving prediction games with parallel batch gradient descent.
  Proceedings of the European Conference on Machine Learning 2015.

2014

• Niels Landwehr, Sebastian Arzt, Tobias Scheffer, and Reinhold Kliegl.
  A Model of Individual Differences in Gaze Control During Reading.
  Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 2014.
• Michael Großhans, Christoph Sawade, Tobias Scheffer, and Niels Landwehr.
  Joint Prediction of Topics in a URL Hierarchy.
  Proceedings of the 25th European Conference on Machine Learning. 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. 
  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

• Tobias Scheffer and Stefan Wrobel.
  A sequential sampling algorithm for a general class of utility criteria.
  Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.
• Tobias Scheffer.
  Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees.
  Proceedings of the Eleventh International Conference on Algorithmic Learning Theory. Sydney, 2000.
• Tobias Scheffer.
  Predicting the Relation between Model Class, Domain, and Error Rate (invited talk).
  What Works Well Where: Proceedings of the ICML-Workshop ,  2000.
• Tobias Scheffer.
  Predicting the generalization performance of cross validatory model selection criteria. Extended version.
  Proceedings of the Seventeenth International Conference on Machine Learning. Stanford, 2000. 
• Tobias Scheffer.
  Nonparametric Regularization of Decision Trees .
  European Conference on Machine Learning. Barcelona, Spain. 2000.

1999

• Tobias Scheffer.
  Error Estimation and Model Selection.
  Künstliche Intelligenz, 1999.
• Andrew Mitchell, Tobias Scheffer, Arun Sharma, and Frank Stephan.
  The VC-dimension of subclasses of pattern languages .
  Algorithmic Learning Theory. Tokyo, 1999.
• Tobias Scheffer and Thorsten Joachims.
  Preprint of the full paper on Expected Error Analysis for Model Selection.
• Tobias Scheffer and Thorsten Joachims.
  Expected Error Analysis for Model Selection.
  Proceedings of the International Conference on Machine Learning, 1999 .
• Tobias Scheffer.
  Error Estimation and Model Selection. Postscript; PDF.
  Infix, Sankt Augustin, 1999.  ISBN 3-89601-225-8. 

1998

• Tobias Scheffer and Thorsten Joachims.
  Estimating the expected error of empirical minimizers for model selection. Abstract.  Pre-print of full paper.
  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

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