Volker Tresp

Research Interests

• Machine Learning
• Semantic Web, Linked Data
  
• Statistical Relational Learning
• Gaussian Processes, Dirichlet Processes
  
• Neural Networks,  Pattern Recognition
  
• Graphical Models, Bayesian Networks
• Stochastical Systems and Time Series
• User Modelling
• Bioinformatics
• Information Extraction, Information Retrieval
• Medical Decision Support Systems
• Reinforcement Learning and Multi-Agent Systems

Biography

I received a Diploma degree in physics from the University of Göttingen,  Germany, in 1984  and the M.Sc. and Ph.D. degrees from Yale University, New Haven, CT, in 1986 and 1989 respectively.  Since 1989 I am the head of a research team in machine learning at Siemens CT.  In 1994 I was a visiting scientist at the Massachusetts Institute of Technology's Center for Biological and Computational Learning.  Each summer I give a lecture on  machine learning and datamining (since 2003). In my projects I collaborate with both the Ludwig Maximilian University of Munich (Ludwig-Maximilians-Universität München, LMU) and the Technical University Munich  (Technische Universität München, TUM). 

E-mail: volker.tresp at s i e m e n s.com

Student(s) I work with

• Maximilian Nickel, Ludwig Maximilian University of Munich
• Yi Huang, Ludwig Maximilian University of Munich

Past students I have worked with

• Achim Rettinger
  
• Markus Bundschus
  
• Zhao Xu
  
• Shipeng Yu
  
• Kai Yu
  
• Anton Schwaighofer
  
• Thomas Briegel
• Derrick Pisani
  
• Harald Steck
  
• Jaakko Hollmen
  
• Dirk Ormoneit
• Reimar Hofmann
  
• Michael Haft
  
• Jürgen Hollatz
  
• Ralph Neuneier
  

Tutorials

• Volker Tresp. Multivariate Models for Relational Learning. Tutorial at the ILP 2010, 2010
• Volker Tresp and Kai Yu. Learning with dependencies between several response variables: From hierarchical bayes and multitask learning to structured prediction and relational learning. Tutorial at the ICML 2009, 2009. .
• Volker Tresp. Dirichlet processes and nonparametric bayesian modelling. Tutorial at the Machine Learning Summer School 2006 in Canberrra, Australia, 2006.
  

Papers

• Davide Magatti, Florian Steinke, Markus Bundschus, and Volker Tresp. Combined structured and keyword-based search in textually enriched entity-relationship graphs. First workshop on automated knowledge base construction (AKBC 2010). 
  

• Markus Bundschus, Volker Tresp, and Hans-Peter Kriegel. Topic models for semantically annotated document collections. In NIPS 2009 Workshop: Applications for Topic Models: Text and Beyond, 2009.
• Markus Bundschus, Shipeng Yu, Volker Tresp, Achim Rettinger, Matthaeus Dejori, and Hans-Peter Kriegel. Hierarchical bayesian models for collaborative tagging systems. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 2009.
• Achim Rettinger, Matthias Nickles, and Volker Tresp. Statistical relational learning with formal ontologies. In Proceedings of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2009.
• Volker Tresp, Yi Huang, Markus Bundschus, and Achim Rettinger. Materializing and querying learned knowledge. In Proceedings of the First ESWC Workshop on Inductive Reasoning and Machine Learning on the Semantic Web (IRMLeS 2009), 2009.
• Zhao Xu, Kristian Kersting, and Volker Tresp. Multi-relational learning with gaussian processes. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), July 2009. 
• Zhao Xu, Volker Tresp, Achim Rettinger, and Kristian Kersting. Social network mining with nonparametric relational models. In H. Zhang, M. Smith, L. Giles, and J. Yen, editors, Advances in Social Network Mining and Analysis, LNCS. Springer, 2009. 

• Markus Bundschus, Matthaeus Dejori, Martin Stetter, Volker Tresp, and Hans-Peter Kriegel. Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics, 9:207, 2008. 
• Markus Bundschus, Matthaeus Dejori, Shipeng Yu, Volker Tresp, and Hans-Peter Kriegel. Statistical modeling of medical indexing processes for biomedical knowledge information discovery from text. In Proceedings of the 8th International Workshop on Data Mining in Bioinformatics (BIOKDD '08), 2008. 
• Dieter Fensel, Frank van Harmelen, Bo Andersson, Paul Brennan, Hamish Cunningham, Emanuele Della Valle, Florian Fischer, Zhisheng Huang, Atanas Kiryakov, Tony Kyung il Lee, Lael Schooler, Volker Tresp, Stefan Wesner, Michael Witbrock, and Ning Zhong. Towards larkc: A platform for web-scale reasoning. In Proceedings of the 2th IEEE International Conference on Semantic Computing (ICSC 2008),, 2008.
• Christoph Lippert, Stefan-Hagen Weber, Yi Huang, Volker Tresp, Matthias Schubert, and Hans-Peter Kriegel. Relation-prediction in multi-relational domains using matrix-factorization. In NIPS 2008 Workshop: Structured Input - Structured Output, 2008. 
• Stefan Reckow and Volker Tresp. Integrating ontological prior knowledge into relational learning. In NIPS 2008 Workshop: Structured Input - Structured Output, 2008.
• Achim Rettinger, Matthias Nickles, and Volker Tresp. A statistical relational model for trust learning. In Proceeding of 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), 2008.
• Volker Tresp, Markus Bundschus, Achim Rettinger, and Yi Huang. Towards machine learning on the semantic web. Technical report, 2008. Extended Version of a paper to appear in: Costa, Paulo C. G.; D'Amato, Claudia; Fanizzi, Nicola; Laskey, Kathryn B.; Laskey, Kenneth J.; Lukasiewicz, Thomas; Nickles, Matthias; and Pool, Michael (Eds.): Uncertainty Reasoning for the Semantic Web I Lecture Notes in AI, Springer, 2008.
  
• Zhao Xu, Volker Tresp, Shipeng Yu, and Kai Yu. Nonparametric relational learning for social network analysis. In 2nd ACM Workshop on Social Network Mining and Analysis (SNA-KDD 2008), 2008. 

• Achim Rettinger, Matthias Nickles, and Volker Tresp. Learning initial trust among interacting agents. In Eleventh International Workshop CIA 2007 on Cooperative Information Agents. Springer 2007, September 2007.
• Anton Schwaighofer, Mathaeus Dejori, Volker Tresp, and Martin Stetter. Structure learning with nonparametric decomposable models. In Proceedings of ICANN 2007. Springer Verlag, 2007. 
• Zhao Xu, Volker Tresp, Shipeng Yu, Kai Yu, and Hans-Peter Kriegel. Fast inference in infinite hidden relational models. In 5th International Workshop on Mining and Learning with Graphs (MLG 2007), 2007. . 
• Shipeng Yu, Volker Tresp, and Kai Yu. Robust multi-task learning with t-processes. In 24th International Conference on Machine Learning (ICML'2007), 2007.
• Yi Huang, Volker Tresp, and Stefan Hagen Weber. Predictive Modeling using Features derived from Paths in Relational Graphs. Technical report, 2007.

• Zhao Xu, Volker Tresp, Kai Yu, and Hans-Peter Kriegel. Infinite hidden relational models. In Proceedings of the 22nd International Conference on Uncertainty in Artificial Intelligence (UAI 2006), 2006. 
• Kai Yu, Jinbo Bi, and Volker Tresp. Active learning via transductive experimental design. In The 23nd International Conference on Machine Learning (ICML 2006), 2006.
• Kai Yu, Wei Chu, Shipeng Yu, Volker Tresp, and Zhao Xu. Stochastic relational models for discriminative link prediction. In Advances in Neural Information Processing Systems (NIPS*2006). MIT Press, 2006. 
• Shipeng Yu, Kai Yu, and Volker Tresp. Collaborative ordinal regression. In The 23nd International Conference on Machine Learning (ICML 2006), 2006.
• Shipeng Yu, Kai Yu, Volker Tresp, and Hans-Peter Kriegel. Multi-output regularized feature projection. IEEE Transactions on Knowledge and Data Engineering, 18 (22), 2006.
• Shipeng Yu, Kai Yu, Volker Tresp, and Hans-Peter Kriegel. Variational bayesian dirichlet-multinomial allocation for exponential family mixtures. In 17th European Conference on Machine Learning (ECML 2006), 2006.
• Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel, and Mingrui Wu. Supervised probabilistic principal component analysis. In 12th ACM International Conference on Knowledge Discovery and Data Mining (KDD 2006), 2006.

• Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, and Hans-Peter Kriegel. Dirichlet enhanced relational learning. In The 22nd International Conference on Machine Learning (ICML 2005), 2005.
• Kai Yu and Volker Tresp. Learning to learn and collaborative filtering. In Workshop on Inductive Transfer: 10 Years Later (NIPS*2005 Workshop), 2005. 
• Kai Yu and Volker Tresp. Soft clustering on graphs. In Advances in Neural Information Processing Systems (NIPS*2005). MIT Press, 2005. 
• Kai Yu, Volker Tresp, and Anton Schwaighofer. Learning gaussian processes from multiple tasks. In The 22nd International Conference on Machine Learning (ICML 2005), 2005. 
• Kai Yu, Shipeng Yu, and Volker Tresp. Blockwise supervised inference on large graphs. In Proceedings of Workshop on Learning with Partially Classified Training Data at the 22nd International Conference on Machine Learning (ICML 2005), 2005. 
• Kai Yu, Shipeng Yu, and Volker Tresp. Dirichlet enhanced latent semantic analysis. In Worksjop on Artificial Intelligence and Statistics AISTAT 2005, 2005. 
• Kai Yu, Shipeng Yu, and Volker Tresp. Multi-label informed latent semantic indexing. In Proceedings of the 28th Annual International ACM SIGIR Conference, 2005. 
• Kai Yu, Shipeng Yu, and Volker Tresp. Multi-output regularized projection. In IEEE Computer Society International Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2005.  
• Shipeng Yu, Kai Yu, Volker Tresp, and Hans-Peter Kriegel. A probabilistic clustering-projection model for discrete data. In Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2005), 2005. 

• Mathäus Dejori, Anton Schwaighofer, Volker Tresp, and Martin Stetter. Mining functional modules in genetic networks with decomposable graphical models. OMICS A Journal of Integrative Biology, 8(2):176-188, 2004. 
• Anton Schwaighofer, Volker Tresp, and Kai Yu. Learning gaussian process kernels via hierarchical bayes. In Advances in Neural Information Processing Systems (NIPS*2004). MIT Press, 2004. 
• Volker Tresp and Kai Yu. An introduction to nonparametric hierarchical bayesian modelling with a focus on multi-agent learning. In Proceedings of the Hamilton Summer School on Switching and Learning in Feedback Systems. Lecture Notes in Computing Science, 2004. 
• Kai Yu, Anton Schwaighofer, Volker Tresp, Xiaowei Xu, and Hans-Peter Kriegel. Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engineering (TKDE), 10, 2004. 
• Kai Yu, Volker Tresp, and Shipeng Yu. A nonparametric hierarchical bayesian framework for information filtering. In Proceedings of the 27th Annual International ACM SIGIR Conference. ACM, 2004. 

• Kai Yu Kai, Anton Schwaighofer, Volker Tresp, Wei-Ying Ma, and HongJiang Zhang. Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical bayes. In Proceedings of 19th International Conference on Uncertainty in Artificial Intelligence (UAI'03)), 2003.
• Anton Schwaighofer, Marian Grigoras, Volker Tresp, and Clemens Hoffmann. Gpps: A gaussian process positioning system for cellular networks. In Advances in Neural Information Processing Systems (NIPS*2003). MIT Press, 2003.
• Zhao Xu, Kai Yu, Volker Tresp, Xiaowei Xu, and Jizhi Wang. Representative sampling for text classification using support vector machines. In 25th European Conference on Information Retrieval Research, ECIR'2003, 2003. 
• Kai Yu, Wei-Ying Ma, Volker Tresp, Zhao Xu, Xiaofei He, HongJiang Zhang, and Hans-Peter Kriegel. Knowing a tree from the forest: Art image retrieval using a society of profiles. In Proceedings of 11th Annual ACM International Conference on Multimedia (ACM Multimedia'03), 2003.

• Thomas Briegel and Volker Tresp. A nonlinear state space model for the blood glucose metabolism of a diabetic. at-Automatisierungstechnik, 50, 2002.
• Alexander K. Scheel, Andreas Krause, Ingolf Mesecke von Rheinbaben, Georg Metzger, Helmut Rost, Volker Tresp, Peter Mayer, Monika Reuss-Borst, and Gerhard A. Müller. Assessment of Proximal Finger Joint Inflammation in Patients With Rheumatoid Arthritis, Using a Novel Laser-Based Imaging Technique. Arthritis and Rheumatism, 46(5), 2002.
• Anton Schwaighofer and Volker Tresp. Transductive and inductive methods for approximate gaussian process regression. In Advances in Neural Information Processing Systems (NIPS*2002). MIT Press, 2002.
• Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, and Gerhard A. Müller. The RA scanner: prediction of rheumatoid joint inflammation based on laser imaging. In Advances in Neural Information Processing Systems (NIPS*2002). MIT Press, 2002.
• Volker Tresp. The equivalence between row and column linear regression. Technical report, 2002. 
• Christopher K. I. Williams, Carl Edward Rasmussen, Anton Schwaighofer, and Volker Tresp. Observations of the nyström method for gaussiam process prediction. Technical report, University of Edinburgh, 2002. 

• Anton Schwaighofer and Volker Tresp. The Bayesian committee support vector machine. In International Conference on Artificial Neural Networks - ICANN 2001, 2001. 
• Volker Tresp. Committee machines. In Yu Hen Hu and Jenq-Nen Hwang, editors, Handbook for Neural Network Signal Processing. CRC Press, 2001. 
• Volker Tresp. Scaling kernel-based systems to large data sets. Data Mining and Knowledge Discovery, 5, 2001. 
• Volker Tresp and Anton Schwaighofer. Local factorization of functions. Technical report, 2001. 
• Volker Tresp and Anton Schwaighofer. Scalable kernel systems. In International Conference on Artificial Neural Networks - ICANN 2001, 2001. 

• Thomas Briegel and Volker Tresp. Dynamic neural regression models. Technical report, Instituts für Statistik der Ludwig-Maximilians-Universität München, 2000. Discussion Paper 181.
• Volker Tresp. A bayesian committee machine. Neural Computation, 12, 2000. 
• Volker Tresp. The generalized bayesian committee machine. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2000.
• Volker Tresp. Mixtures of gaussian processes. In Advances in Neural Information Processing Systems (NIPS*2000). MIT Press, 2000. 
• Volker Tresp, Thomas Briegel, and John Moody. Neural-network models for the blood glucose metabolism of a diabetic. IEEE Transactions on Neural Networks, 10, 2000. 

• Thomas Briegel and Volker Tresp. Robust neural network regression for offline and online learning. In Advances in Neural Information Processing Systems (NIPS*1999). MIT Press, 1999.
• Michael Haft, Reimar Hofmann, and Volker Tresp. Model-independent mean field theory as a local method for approximate propagation of information. Network: Computation in Neural Systems, 10, 1999. 
• Volker Tresp, Michael Haft, and Reimar Hofmann. Mixture approximations to bayesian networks. In K. B. Laskey and H. Prade, editors, Uncertainty in Artificial Intelligence, Proceedings of the Fifteenth Conference. Morgan Kaufmann Publishers, 1999.
• Harald Steck and Volker Tresp. Bayesian Belief Networks for Data Mining, Proceedings of the 2nd Workshop on Data Mining und Data Warehousing, DWDW99, 1999.
  

• Thomas Briegel and Volker Tresp. Fisher scoring and a mixture of modes approach for approximate inference and learning in nonlinear state space models. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems (NIPS*1998). MIT Press, 1998
• .Jaakko Hollmén and Volker Tresp. Call-based fraud detection in mobile communication networks using a hierarchical regime-switching model. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems (NIPS*1998). MIT Press, 1998. 
• Dirk Ormoneit and Volker Tresp. Averaging, maximum penalized likelihood and bayesian estimation for improving gaussian mixture probability density estimates. IEEE Transactions on Neural Networks, 9, 1998.
• Volker Tresp and Reimar Hofmann. Nonlinear time-series prediction with missing and noisy data. Neural Computation, 1998. 

• Thomas Briegel and Volker Tresp. A solution for missing data in recurrent neural networks with an application to blood glucose prediction. In M. I. Jordan, M. S. Kearns, and S. A. Solla, editors, Advances in Neural Information Processing Systems (NIPS*1997), 1997. 
• Reimar Hofmann and Volker Tresp. Nonlinear markov networks for continuous variables. In M. I. Jordan, M. S. Kearns, and S. A. Solla, editors, Advances in Neural Information Processing Systems (NIPS*1997). MIT Press, 1997. 
• Michiaki Taniguchi and Volker Tresp. Averaging regularized estimator. Neural Computation, 1997. 
• Volker Tresp, Jürgen Hollatz, and Subutai Ahmad. Representing probabilistic rules with networks of gaussian basis functions. Machine Learning, 1997.

• Volker Tresp, Ralph Neuneier, and Hans-Georg Zimmermann. Early brain damage. In M. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems (NIPS*1996). MIT Press, 1996.
  

• Reimar Hofmann and Volker Tresp. Discovering structure in continuous variables using bayesian networks. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems (NIPS*1995). MIT Press, 1995. 
• Dirk Ormoneit and Volker Tresp. Improved gaussian mixture density estimates using bayesian penalty terms und network averaging. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems (NIPS*1995). MIT Press, 1995.
• Volker Tresp and Reimar Hofmann. Missing and noisy data in nonlinear time-series prediction. In Neural Networks for Signal Processing 5. IEEE Signal Processing Society, 1995. 

• Volker Tresp and Michiaki Taniguchi. Combining estimators using non-constant weighting functions. In G. Tesauro, D. S. Touretzky, and Leen T. K., editors, Advances in Neural Information Processing Systems (NIPS*1994). MIT press, 1994. 
• Volker Tresp, Ralph Neuneier, and Subutai Ahmad. Efficient methods for dealing with missing data in supervised learning. In G. Tesauro, D. S. Touretzky, and Leen T. K., editors, Advances in Neural Information Processing Systems (NIPS*1994). MIT Press, 1994. 

• Volker Tresp, Subutai Ahmad, and Ralph Neuneier. Training neural networks with deficient data. In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems (NIPS*1993). Morgan Kaufmann, 1993.

• Subutai Ahmad and Volker Tresp. Some solutions to the missing feature problem in vision. In C. L. Giles, Hanson S. J., and Cowan J. D., editors, Advances in Neural Information Processing Systems (NIPS*1992). Morgan Kaufman, 1992
• Volker Tresp, Jürgen Hollatz, and Subutai Ahmad. Network structuring and training using rule-based knowledge. In C. L. Giles, Hanson S. J., and Cowan J. D., editors, Advances in Neural Information Processing Systems (NIPS*1992). Morgan Kaufman, 1992.
• Volker Tresp, Ira Leuthäusser, Martin Schlang, Ralph Neuneier, Klaus Abraham-Fuchs, and Wolfgang Härer. The neural impulse response filter. In International Conference on Artificial Neural Networks II. North Holland, 1992. 

• Martin Röscheisen, Reimar Hofmann, and Volker Tresp. Neural control for rolling mills: Incorporating domain theories to overcome data deficiency. In J. E. Moody, Hanson S. J., and Lippmann R., editors, Advances in Neural Information Processing Systems (NIPS*1991). Morgan Kaufmann, 1991.

• Volker Tresp. Neural network approach for three-dimensional object recognition. In Richard Lippmann, John E. Moody, and David S. Touretzky, editors, Advances in Neural Information Processing Systems (NIPS*1990). Morgan Kaufmann, 1990.