Thore Graepel
SENIOR RESEARCHER
.
I am a senior researcher at Microsoft Research Cambridge leading the Online Services and Advertising and Applied Games group together with Ralf Herbrich. Our work is focused on the application of large scale machine learning and probabilistic modelling techniques to a wide range of problems including online advertising, web search, and games. I have a particular passion for the game of go and the quest for developing a go engine that plays as good as the best human players.
Before joining the Cambridge lab of Microsoft Research, I was a postdoctoral researcher at the Department of Computer Science at Royal Holloway, University of London working on learning theory and machine learning algorithms with Prof. John Shawe-Taylor. View the beautiful campus of Royal Holloway here.
Before that, I worked with Nici Schraudolph and Prof. Petros Koumoutsakos as a postdoctoral researcher at the Institute of Computational Science (ICOS) which is part of the Department of Computer Science of the Swiss Federal Institute of Technology, Zürich (ETH). Topics of research were machine learning and large-scale nonlinear optimisation.
I received my doctorate (Dr. rer. nat) from the Department of Computer Science of the Technical University of Berlin, where I was first a member of the Neural Information Processing group of Prof. Klaus Obermayer and later joined the Statistics group of Prof. Ulrich Kockelkorn. My homepage from the good old days at TU Berlin can be found here.
Contact:
Thore Graepel
Microsoft Research Ltd
Roger Needham Building
7 J J Thomson Avenue
Cambridge CB3 0FB, U.K.
Tel. +44 (0)1223 479 759
Fax: +44 (0)1223 479 999
thoreg@microsoft.com
http://research.microsoft.com/en-us/people/thoreg
Publications
- Anton Schwaighofer, Joaquin Quinonero Candela, Thomas Borchert, Thore Graepel, and Ralf Herbrich, Scalable Clustering and Keyword Suggestion for Online Advertisements, in Proceedings of ADKDD 2009: 3rd Annual International Workshop on Data Mining and Audience Intelligence for Advertising, Association for Computing Machinery, Inc., 2009
- David Stern, Ralf Herbrich, and Thore Graepel, Matchbox: Large Scale Bayesian Recommendations, in Proceedings of the 18th International World Wide Web Conference, 2009
- Pierre Dangauthier, Ralf Herbrich, Tom Minka, and Thore Graepel, TrueSkill Through Time: Revisiting the History of Chess, in Advances in Neural Information Processing Systems 20, MIT Press, 2008
- Ralf Herbrich, Thore Graepel, and Brendan Murphy, Structure from Failure, in Second Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML07) , USENIX, June 2007
- David Stern, Ralf Herbrich, and Thore Graepel, Learning To Solve Game Trees, in Proceedings of the International Conference of Machine Learning, January 2007
- Ralf Herbrich, Tom Minka, and Thore Graepel, TrueSkill(TM): A Bayesian Skill Rating System, in Advances in Neural Information Processing Systems 20, MIT Press, January 2007
- Thore Graepel and Ralf Herbrich, Ranking and Matchmaking, in Game Developer Magazine, October 2006
- David Stern, Ralf Herbrich, and Thore Graepel, Bayesian Pattern Ranking for Move Prediction in the Game of Go, in Proceedings of the International Conference of Machine Learning, January 2006
- Ralf Herbrich and Thore Graepel, TrueSkill(TM): A Bayesian Skill Rating System, no. MSR-TR-2006-80, 2006
- Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, and Dan Roth, Generalization Error Bounds for the Area Under the ROC curve, in Journal of Machine Learning Research, vol. 6, pp. 393-425, MIT Press, January 2005
- Thore Graepel, Ralf Herbrich, and John Shawe-Taylor, PAC-Bayesian compression bounds on the prediction error of learning algorithms for classification, in Machine Learning, vol. 59, pp. 55-76, Kluwer Academic , January 2005
- Shyamsundar Rajaram, Thore Graepel, and Ralf Herbrich, Poisson-Networks: A Model for structured point processes, in Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, January 2005
- Ralf Herbrich, Thore Graepel, and Robert C. Williamson, The Structure of Version Space, in Innovations in Machine Learning: Theory and Applications, pp. 257-274, Springer-Verlag, 2005
- Ralf Herbrich, Thore Graepel, and Robert C. Williamson, The Structure of Version Space, no. MSR-TR-2004-63, July 2004
- Thore Graepel, Ralf Herbrich, Andriy Kharechko, and John Shawe-Taylor, Semidefinite Programming by Perceptron Learning, in Advances in Neural Information Processing Systems 16, MIT Press, January 2004
- Shivani Agarwal, Thore Graepel, Ralf Herbrich, and Dan Roth, A Large Deviation Bound for the Area Under the ROC Curve, in Advances in Neural Information Processing Systems 17, MIT Press, January 2004
- David Stern, Thore Graepel, and David MacKay, Modelling Uncertainty in the Game of Go, in Advances in Neural Information Processing Systems 16, January 2004
- Thore Graepel and Ralf Herbrich, Invariant Pattern Recognition by Semidefinite Programming Machines, in Advances in Neural Information Processing Systems 16, MIT Press, January 2004
- Hendrik Purwins, Thore Graepel, Benjamin Blankertz, and Klaus Obermayer, Correspondence analysis for visualizing interplay of pitch class, key, and composer, in Perspectives in Mathematical and Computational Music Theory, pp. 432 - 454, Universities Press, 2004
- Thore Graepel, Ralf Herbrich, and Julian Gold, Learning to Fight, in Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education, January 2004
- Ralf Herbrich and Thore Graepel, Introduction to the Special Issue on Learning Theory, in Journal of Machine Learning Research, vol. 4, pp. 755–757, MIT Press, October 2003
- Malte Kuss and Thore Graepel, The Geometry of Kernel Canonical Correlation Analysis, no. 108, January 2003
- Nicol N. Schraudolph and Thore Graepel, Combining Conjugate Direction Methods with Stochastic Approximation of Gradients, in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, AISTATS 2003, January 2003
- Thore Graepel, Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations, in Proceedings of the Twentieth International Conference on Machine Learning, January 2003
- Ralf Herbrich and Thore Graepel, A PAC-Bayesian Margin Bound for Linear Classifiers, in IEEE Transactions on Information Theory, vol. 48, no. 12, pp. 3140–3150, January 2002
- Thore Graepel and Nicol N. Schraudolph, Stable adaptive momentum for rapid online learning in nonlinear systems, in Proceedings of the International Conference on Neural Networks, ICANN 2002, Springer, January 2002
- Nicol N. Schraudolph and Thore Graepel, Towards Stochastic Conjugate Gradient Methods, in Proceedings of the 9th International Conference on Neural Information Processing, ICONIP 2002, January 2002
- Thore Graepel, Kernel matrix completion by semidefinite programming, in Proceedings of the International Conference on Neural Networks, ICANN 2002, Springer, January 2002
- Nicol N. Schraudolph and Thore Graepel, Conjugate Directions for Stochastic Gradient Descent, in Proceedings of the International Conference on Neural Networks, ICANN 2002, Springer, January 2002
- Ralf Herbrich, Thore Graepel, and Colin Campbell, Bayes Point Machines, in Journal of Machine Learning Research, vol. 1, pp. 245-279, MIT Press, January 2001
- Thore Graepel and Ralf Herbrich, A PAC-Bayesian Margin Distribution Bound for Kernel Classifiers (extended abstract), January 2001
- Sambu Seo, Marko Wallat, Thore Graepel, and Klaus Obermayer, Gaussian Process Regression: Active Data Selection and Test Point Rejection, in Proceedings of the International Joint Conference on Neural Networks IJCNN'2000, January 2000
- Thore Graepel and Klaus Obermayer, A Self-Organizing Map for Proximity Data, in Neural Computation, vol. 11, pp. 139-155, January 1999
- Thore Graepel, Statistical Physics of Clustering Algorithms, Berlin, Germany, January 1998
- Thore Graepel, Matthias Burger, and Klaus Obermayer, Self-Organizing Maps: Generalizations and New Optimization Techniques, in Neurocomputing, vol. 20, pp. 173-190, January 1998
- Thore Graepel, Matthias Burger, and Klaus Obermayer, Deterministic Annealing for Topographic Vector Quantization and Self-Organizing Maps, in Proceedings of the Workshop on Self-Organizing Maps WSOM`97, January 1997
- Matthias Burger, Thore Graepel, and Klaus Obermayer, Phase Transitions in Soft Topographic Vector Quantization, in Artificial Neural Networks – ICANN97, Springer-Verlag, January 1997
- Thore Graepel, Matthias Burger, and Klaus Obermayer, Phase Transitions in Stochastic Self-Organizing Maps, in Physical Review E, vol. 56, no. 4, pp. 3876-3890, January 1997
