Volker Tresp

 

Research Interests


Biography

I received a Diploma degree in physics from the University of Goettingen,
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 in a large international company. In 1994 I was avisiting scientist at the
Massachusetts Institute of Technology's Center for Biological and Computational Learning.
Each summer  (since 2003) I am giving a  lecture on
machine learning and datamining at the University of Munich.

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

Student(s) I work with


Past students I have worked with

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Papers
  74 references, last updated Wed Sep 24 10:23:03 MET 2008

[1]
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. (PDF, 465294 bytes)

[2]
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. (PDF, 313993 bytes)

[3]
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. (PDF, 739623 bytes)

[4]
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. (PDF, 1006113 bytes)

[5]
Volker Tresp, Markus Bundschus, Achim Rettinger, and Yi Huang. Towards machine learning on the semantic web. Technical report, Siemens AG, 2008. Extended Version of a paper to appear in: Uncertainty Reasoning for the Semantic Web I Lecture Notes in AI, Springer. (PDF, 268646 bytes)

[6]
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. (PDF, 456715 bytes)

[7]
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. (PDF, 322585 bytes)

[8]
Anton Schwaighofer, Mathaeus Dejori, Volker Tresp, and Martin Stetter. Structure learning with nonparametric decomposable models. In Proceedings of ICANN 2007. Springer Verlag, 2007. (PDF, 391200 bytes)

[9]
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. (PDF, 269100 bytes)

[10]
Kai Yu, Wei Chu, Yu Yu, Volker Tresp, and Zhao Xu. Stochastic relational models for discriminative link prediction. In Advances in Neural Information Processing Systems 19. MIT Press, 2007. (PDF, 159290 bytes)

[11]
Shipeng Yu, Volker Tresp, and Yu Kai. Robust multi-task learning with t-processes. In 24th International Conference on Machine Learning (ICML'2007), 2007. (PDF, 1284927 bytes)

[12]
Volker Tresp. Dirichlet processes and nonparametric bayesian modelling. In Tutorial at the Machine Learning Summer School 2006 in Canberrra, Australia, 2006. (PDF, 2583431 bytes)

[13]
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. (PDF, 286828 bytes)

[14]
Kai Yu and Volker Tresp. Soft clustering on graphs. In Advances in Neural Information Processing Systems 18. MIT Press, 2006. (PDF, 442431 bytes)

[15]
Kai Yu, Jinbo Bi, and Volker Tresp. Active learning via transductive experimental design. In The 23nd International Conference on Machine Learning (ICML 2006), 2006. (PDF, 620905 bytes)

[16]
Shipeng Yu, Kai Yu, and Volker Tresp. Collaborative ordinal regression. In The 23nd International Conference on Machine Learning (ICML 2006), 2006. (PDF, 377968 bytes)

[17]
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.

[18]
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. (PDF, 418307 bytes)

[19]
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. (PDF, 382762 bytes)

[20]
Anton Schwaighofer, Volker Tresp, and Kai Yu. Learning gaussian process kernels via hierarchical bayes. In Advances in Neural Information Processing Systems 17. MIT Press, 2005. (PDF, 249844 bytes)

[21]
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. (PDF, 360537 bytes)

[22]
Kai Yu and Volker Tresp. Learning to learn and collaborative filtering. In Workshop on Inductive Transfer: 10 Years Later (NIPS*05 Workshop), 2005. (PDF, 120698 bytes)

[23]
Kai Yu, Volker Tresp, and Anton Schwaighofer. Learning gaussian processes from multiple tasks. In The 22nd International Conference on Machine Learning (ICML 2005), 2005. (PDF, 281281 bytes)

[24]
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. (PDF, 430757 bytes)

[25]
Kai Yu, Shipeng Yu, and Volker Tresp. Dirichlet enhanced latent semantic analysis. In Worksjop on Artificial Intelligence and Statistics AISTAT 2005, 2005. (PDF, 538344 bytes)

[26]
Kai Yu, Shipeng Yu, and Volker Tresp. Multi-label informed latent semantic indexing. In Proceedings of the 28th Annual International ACM SIGIR Conference, 2005. (PDF, 223107 bytes)

[27]
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. (PDF, 189810 bytes)

[28]
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. (PDF, 366682 bytes)

[29]
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. (PDF, 279114 bytes)

[30]
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 16. MIT Press, 2004. (PDF, 224000 bytes)

[31]
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. (PDF, 304947 bytes)

[32]
Kai Yu, Anton Schwaighofer, Volker Tresp, X. Xu, and Hans-Peter Kriegel. Probabilistic memory-based collaborative filtering. IEEE Transactions on Knowledge and Data Engineering (TKDE), 10, 2004. (PDF, 770767 bytes)

[33]
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. (PDF, 400060 bytes)

[34]
Anton Schwaighofer and Volker Tresp. Transductive and inductive methods for approximate gaussian process regression. In Advances in Neural Information Processing Systems 15. MIT Press, 2003. (PDF, 70794 bytes)

[35]
Anton Schwaighofer, Volker Tresp, Peter Mayer, Alexander K. Scheel, and Gerhard A. Müller. Prediction of rheumatoid joint inflammation based on laser imaging. In Advances in Neural Information Processing Systems 15. MIT Press, 2003. (PDF, 204851 bytes)

[36]
Z. Xu, Kai Yu, Volker Tresp, X. Xu, and J. Wang. Representative sampling for text classification using support vector machines. In 25th European Conference on Information Retrieval Research, ECIR'2003, 2003. (PDF, 369921 bytes)

[37]
Kai Yu, W.-Y. Ma, Volker Tresp, Z. Xu, X. He, H. J. 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. (PDF, 4005618 bytes)

[38]
Kai Yu, Anton Schwaighofer, Volker Tresp, W.-Y. Ma, and H. J. 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. (PDF, 329335 bytes)

[39]
T. Briegel and V. Tresp. A nonlinear state space model for the blood glucose metabolism of a diabetic. at-Automatisierungstechnik, 50, 2002. (PDF, 294691 bytes)

[40]
A. K. Scheel, I. Mesecke-von Rheinbaben, A. Krause, G. Metzger, H. Rost, P. Mayer, V. Tresp, G. A. Müller, and M. Reuss-Borst. A preliminary clinical study with a novel laser-based imaging technique in rheumatoid arthritis. Arthritis and Rheumatism, 46(5), 2002. (PDF, 296172 bytes)

[41]
Volker Tresp. The equivalence between row and column linear regression. Technical report, 2002. (PDF, 123004 bytes)

[42]
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. (Gzipped PostScript, 9 pages, 98202 bytes) (PDF, 226240 bytes)

[43]
Anton Schwaighofer and Volker Tresp. The Bayesian committee support vector machine. In Artificial Neural Networks - ICANN 2001, 2001. (Gzipped PostScript, 7 pages, 56444 bytes) (PDF, 97112 bytes)

[44]
V. Tresp. Mixtures of gaussian processes. In Advances in Neural Information Processing Systems 13. MIT Press, 2001. (Gzipped PostScript, 7 pages, 53352 bytes) (PDF, 121351 bytes)

[45]
Volker Tresp. Committee machines. In Yu Hen Hu and Jenq-Nen Hwang, editors, Handbook for Neural Network Signal Processing. CRC Press, 2001. (Gzipped PostScript, 21 pages, 109449 bytes) (PDF, 163877 bytes)

[46]
Volker Tresp. Scaling kernel-based systems to large data sets. Data Mining and Knowledge Discovery, 5, 2001. (Gzipped PostScript, 18 pages, 90895 bytes) (PDF, 156683 bytes)

[47]
Volker Tresp and Anton Schwaighofer. Local factorization of functions. Technical report, 2001. (PDF, 293584 bytes)

[48]
Volker Tresp and Anton Schwaighofer. Scalable kernel systems. In Artificial Neural Networks - ICANN 2001, 2001. (Gzipped PostScript, 7 pages, 49680 bytes) (PDF, 90877 bytes)

[49]
T. Briegel and V. Tresp. Robust neural network regression for offline and online learning. In Advances in Neural Information Processing Systems 12. MIT Press, 2000. (Gzipped PostScript, 7 pages, 73493 bytes) (PDF, 152041 bytes)

[50]
Briegel. T. and V. Tresp. Dynamic neural regression models. Technical report, Instituts für Statistik der Ludwig-Maximilians-Universität München, 2000. Discussion Paper 181. (Gzipped PostScript, 34 pages, 216047 bytes) (PDF, 381940 bytes)

[51]
V. Tresp. The generalized bayesian committee machine. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2000, 2000. (Gzipped PostScript, 10 pages, 81710 bytes) (PDF, 143465 bytes)

[52]
Volker Tresp. A bayesian committee machine. Neural Computation, 12, 2000. (Gzipped PostScript, 25 pages, 174519 bytes) (PDF, 466800 bytes)

[53]
V. Tresp, T. Briegel, and J. Moody. Neural-network models for the blood glucose metabolism of a diabetic. IEEE Transactions on Neural Networks, 10, 2000. (Gzipped PostScript, 24 pages, 94983 bytes) (PDF, 252744 bytes)

[54]
T. Briegel and V. 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 11. MIT Press, 1999. (Gzipped PostScript, 7 pages, 41066 bytes) (PDF, 106065 bytes)

[55]
M. Haft, R. Hofmann, and V. Tresp. Model-independent mean field theory as a local method for approximate propagation of information. Network: Computation in Neural Systems, 10, 1999. (Gzipped PostScript, 19 pages, 123898 bytes) (PDF, 113956 bytes)

[56]
J. Hollmén and V. 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 11. MIT Press, 1999. (Gzipped PostScript, 7 pages, 75664 bytes) (PDF, 139376 bytes)

[57]
V Tresp, Haft M.  , and Hofmann R. 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. (Gzipped PostScript, 8 pages, 58627 bytes) (PDF, 126658 bytes)

[58]
T. Briegel and V. 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 10, 1998. (Gzipped PostScript, 8 pages, 88856 bytes) (PDF, 73814 bytes)

[59]
R. Hofmann and V. 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 10. MIT Press, 1998. (Gzipped PostScript, 8 pages, 38830 bytes) (PDF, 83431 bytes)

[60]
D. Ormoneit and V. Tresp. Averaging, maximum penalized likelihood and bayesian estimation for improving gaussian mixture probability density estimates. IEEE Transactions on Neural Networks, 9, 1998. (Gzipped PostScript, 25 pages, 128589 bytes) (PDF, 407806 bytes)

[61]
V. Tresp and R. Hofmann. Nonlinear time-series prediction with missing and noisy data. Neural Computation, 1998. (Gzipped PostScript, 18 pages, 79038 bytes) (PDF, 158172 bytes)

[62]
M. Taniguchi and V. Tresp. Averaging regularized estimator. Neural Computation, 1997. (Gzipped PostScript, 17 pages, 52442 bytes) (PDF, 131422 bytes)

[63]
V. Tresp, J. Hollatz, and S. Ahmad. Representing probabilistic rules with networks of gaussian basis functions. Machine Learning, 1997. (Gzipped PostScript, 31 pages, 90857 bytes) (PDF, 212216 bytes)

[64]
V. Tresp, R. Neuneier, and H. G. Zimmermann. Early brain damage. In M. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9. MIT Press, 1997. (Gzipped PostScript, 7 pages, 32581 bytes) (PDF, 84548 bytes)

[65]
R. Hofmann and V. 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 8. MIT Press, 1996. (Gzipped PostScript, 7 pages, 39793 bytes) (PDF, 100122 bytes)

[66]
D. Ormoneit and V. 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 8. MIT Press, 1996. (Gzipped PostScript, 7 pages, 87125 bytes) (PDF, 262181 bytes)

[67]
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. (Gzipped PostScript, 12 pages, 168610 bytes) (PDF, 148598 bytes)

[68]
V. Tresp and M 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 7. MIT press, 1995. (Gzipped PostScript, 9 pages, 62616 bytes) (PDF, 102385 bytes)

[69]
V. Tresp., R. Neuneier, and S. 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 7. MIT Press, 1995. (Gzipped PostScript, 8 pages, 41167 bytes) (PDF, 86186 bytes)

[70]
V. Tresp, S. Ahmad, and R. Neuneier. Training neural networks with deficient data. In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems 6. Morgan Kaufmann, 1994. (Gzipped PostScript, 10 pages, 94117 bytes) (PDF, 150258 bytes)

[71]
S. Ahmad and V. 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 5. Morgan Kaufman, 1993. (Gzipped PostScript, 56919 bytes) (PDF, 51493 bytes)

[72]
V. Tresp, J. Hollatz, and S. 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 5. Morgan Kaufman, 1993. (Gzipped PostScript, 10 pages, 53439 bytes) (PDF, 80347 bytes)

[73]
M. Röscheisen, R. Hofmann, and V. 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 4. Morgan Kaufmann, 1992. (Gzipped PostScript, 8 pages, 40629 bytes) (PDF, 175379 bytes)

[74]
Volker Tresp, Ira Leuthäusser, Martin Schlang, Ralph Neuneier, Klaus Abraham-Fuchs, and Wolfgang Härer. The neural impulse response filter. In Advances in Neural Information Processing Systems 16. North Holland, 1992. (PDF, 396461 bytes)