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Dr. Steffen Bickel

Max-Planck-Institut für Informatik
Research Group 2: Machine Learning
Building 46.1, Room 429
Campus E1 4
66123 Saarbrücken
Germany

Research Interests

Learning under Sample Selection Bias and Covariate Shift
Active Learning
Transfer Learning
Multi-Task Learning
Spam Filtering
Hierarchical Bayesian Learning

Publications

Steffen Bickel. Learning under Differing Training and Test Distributions. Ph.D. Thesis. University of Potsdam, Germany, 2009.
Steffen Bickel, Christoph Sawade, and Tobias Scheffer. Transfer Learning by Distribution Matching for Targeted Advertising. Advances in Neural Information Processing Systems (NIPS), 2009.
Steffen Bickel, Jasmina Bogojeska, Thomas Lengauer, and Tobias Scheffer. Multi-Task Learning for HIV Therapy Screening. Proceedings of the International Conference on Machine Learning (ICML), 2008.
Steffen Bickel, Michael Brückner, and Tobias Scheffer. Discriminative learning under covariate shift with a single optimization problem. In J. Quinonero Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, editors, Dataset Shift in Machine Learning. MIT Press, Cambridge, 2008.
Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, and Masashi Sugiyama. Direct density ratio estimation for large-scale covariate shift adaptation. Proceedings of the SIAM International Conference on Data Mining, 2008.
Steffen Bickel, Peter Haider, Tobias Scheffer, and 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.
Steffen Bickel, Michael Brückner, and Tobias Scheffer. Discriminative learning for differing training and test distributions. Proceedings of the International Conference on Machine Learning (ICML), 2007.
Laura Dietz, Steffen Bickel, and Tobias Scheffer. Unsupervised prediction of citation influences. Proceedings of the International Conference on Machine Learning (ICML), 2007.
Steffen Bickel and Tobias Scheffer. Dirichlet-Enhanced Spam Filtering based on Biased Samples. Advances in Neural Information Processing Systems (NIPS), 2007.
Steffen Bickel (editor). Proceedings of the ECML-PKDD Discovery Challenge Workshop, 2006.
Steffen Bickel. ECML-PKDD Discovery Challenge 2006 Overview. Proceedings of the ECML-PKDD Discovery Challenge Workshop, 2006.
Steffen Bickel and Tobias Scheffer. Dirichlet-Enhanced Spam Filtering based on Biased Samples. Proceedings of the ICML Workshop on Learning with Nonparametric Bayesian Methods , 2006.
Steffen Bickel, Volker Tresp, Matthias Seeger (editors). Proceedings of the ICML Workshop on Learning with Nonparametric Bayesian Methods, 2006.
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.
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.
Steffen Bickel and Tobias Scheffer. Mining Email Data. In: John Wang (editor), Encyclopedia of Data Warehousing and Mining, Idea Group, 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.
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 Journal, 6(Suppl 1):S9, April 2005.
Steffen Bickel and Tobias Scheffer. Multi-View Clustering. Proceedings of the IEEE International Conference on Data Mining, 2004.
Steffen Bickel and Tobias Scheffer. Learning from Message Pairs for Automatic Email Answering. Proceedings of the European Conference on Machine Learning, 2004.
A. Abecker, S. Bickel, U. Brefeld, I. Drost, N. Henze, O. Herden, M. Minor, T. Scheffer, L. Stojanovic, S. Weibelzahl (editors). Proceedings of the Workshops on Learning, Knowledge Discovery, and Adaptivity (LWA), 2004.
Steffen Bickel, Ulf Brefeld, Lukas Faulstich, Jörg Hakenberg, Ulf Leser, Conrad 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.
Steffen Bickel. Optimierung von Sicherheitsbeständen in Supply Chains mit Simulation . Diplomarbeit, TU Darmstadt, 2002.

Activities

	2009	Reviewer: NIPS, ICML, KDD.
	2008	Program committee: ECML, PKDD, NIPS Workshop on Cost Sensitive Learning. Reviewer: NIPS, ICML, KDD, IEEE Transactions on Neural Networks.
	2007	Program committee: ICML, ECML, PKDD, PAKDD, ECML-PKDD Discovery Challenge. Reviewer: NIPS, Journal of Machine Learning Research, Dataset Shift in Machine Learning (MIT Press). Project: Scalable Ranking of Online Ads, in cooperation with nugg.ad AG.
	2006	Co-Chair: ICML Workshop on Learning with Nonparametric Bayesian Methods. Chair: ECML-PKDD Discovery Challenge and Discovery Challenge Workshop. Program committee: ACL/Coling, ECML, TMBio. Reviewer: ICML, NIPS, ECAI.
	2005	Program committee: ICML Workshop on Learning with Multiple Views. Reviewer: ICML, ECML, PKDD, DS. Project: Server-Sided Identification of Spam Emails in cooperation with STRATO AG.
	2004	Co-Organizer: Workshops on Learning, Knowledge Discovery, and Adaptivity (LWA-04). Reviewer: Data Mining and Knowledge Discovery Journal, ICML, ECML, ICMLA, DS, BIOKDD, BIDM, FGML.
	2003	Reviewer: Intelligent Data Analysis Journal.

Awards

KDD Cup 2005, Runner-Up Award for Query Categorization Performance,
together with David Vogel, Peter Haider, Rolf Schimpfky, Peter Siemen, Steve Bridges, and Tobias Scheffer.
Winner of the Master's Thesis Award of the German Society for Operations Research, 2003.
Semi-finals of the ACM Programming Contest 1998.

Teaching

Seminar Information Retrieval (SS09)
Seminar Information Retrieval (WS08/09)
Seminar Text Mining und Anwendungen (WS07/08)
HK Maschinelles Lernen und Data Mining (WS06/07)
Seminar Network Mining (WS06/07)
Seminar Text Mining und Anwendungen (WS05/06)
Seminar Text Mining und Anwendungen (WS04/05)
HK Maschinelles Lernen und Data Mining (SS04)
Proseminar Maschinelles Lernen (WS03/04)

Supervised Theses

Barbara Pogorzelska. Transferlernen mit Gauss'schen Prozessen für HIV-Medikamentenselektion, Diplomarbeit, 2008.
Normen Rohde. Qualitätsvalidierung von Zinsstrukturkurven mit statistischen Modellen, Diplomarbeit, 2007.
Rolf Schimpfky. Reputationsanalyse mit Random-Walk-Prozessen, Diplomarbeit, 2006.
Marco Oppel. Akronymgenerierung mit Statistischen Modellen, Studienarbeit, 2006.
Thomas Posner, Interaktive Satzgenerierung mit N-Gramm-Sprachmodellen: Evaluierung der Praxistauglichkeit. Diplomarbeit, 2005.
Birgit Wandrei, Satzvervollständigung mit latent semantischer Analyse. Diplomarbeit, 2005.
Thomas Posner, Nächste-Nachbarn-Klassifikatoren zur automatisierten Email-Beantwortung, Studienarbeit, 2005.
Sebastian Ordyniak, Probabilistisch latent semantisches Indexieren mit mehreren Sichten für Information Retrieval. Studienarbeit, 2005.