Thore Graepel's Home Page

Publications

Publications (no abstract)

Publications (no abstracts)

2007

• Ralf Herbrich, Tom Minka, Thore Graepel. TrueSkill(TM): A Bayesian Skill Rating System 2007 Advances in Neural Information Processing Systems 20

2006

• David Stern, Ralf Herbrich, Thore Graepel. Bayesian Pattern Ranking for Move Prediction in the Game of Go 2006 Proceedings of the International Conference of Machine Learning 873--880
• David Stern, Ralf Herbrich, Thore Graepel. Learning To Solve Game Trees 2006 Proceedings of the International Conference of Machine Learning 873--880

2005

• Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth. A Large Deviation Bound for the Area Under the ROC Curve 2005 Advances in Neural Information Processing Systems 17
• Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth. Generalization Error Bounds for the Area Under the ROC curve 2005 Journal of Machine Learning Research
• Thore Graepel, Ralf Herbrich, John Shawe-Taylor. PAC-Bayesian compression bounds on the prediction error of learning algorithms for classification 2005 Machine Learning
• Shyamsundar Rajaram, Thore Graepel, Ralf Herbrich. Poisson-Networks: A Model for structured point processes 2005 Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics

2004

• Thore Graepel, Ralf Herbrich. Invariant Pattern Recognition by Semidefinite Programming Machines 2004 Advances in Neural Information Processing Systems 16 33--40
• Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor. Semidefinite Programming by Perceptron Learning 2004 Advances in Neural Information Processing Systems 16 457--464
• Thore Graepel, Ralf Herbrich, Julian Gold. Learning to Fight 2004 Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education
• Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth. A Large Deviation Bound for the Area Under the ROC Curve 2004 Advances in Neural Information Processing Systems 17
• David Stern, Thore Graepel, David MacKay. Modelling Uncertainty in the Game of Go 2004 Advances in Neural Information Processing Systems 16 33--40

2003

• Ralf Herbrich, Thore Graepel. Introduction to the Special Issue on Learning Theory 2003 Journal of Machine Learning Research 755--757 4
• Nicol N. Schraudolph, Thore Graepel. Combining Conjugate Direction Methods with Stochastic Approximation of Gradients 2003 Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, AISTATS 2003 Christopher M. Bishop and Brendan Frey
• Hendrik Purwins, Thore Graepel, Benjamin Blankertz, Klaus Obermayer. Correspondence Analysis for Visulaizing Interplay of Pitch Class, Key, and Composer 2003 Perspectives in Mathematical Music Theory
• Thore Graepel. Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations 2003 Proceedings of the Twentieth International Conference on Machine Learning
• Malte Kuss, Thore Graepel. The Geometry of Kernel Canonical Correlation Analysis 2003 Max Planck Institute for Biological Cybernetics, Tuebingen 108
• Hendrik Purwins, Thore Graepel, Benjamin Blankertz, Klaus Obermayer. Correspondence Analysis for Visualizing Interplay of Pitch Class, Key, and Composer 2003 Perspectives in Mathematical Music Theory

2002

• Ralf Herbrich, Thore Graepel. A PAC-Bayesian Margin Bound for Linear Classifiers 2002 IEEE Transactions on Information Theory 12 3140--3150 48
• Nicol N. Schraudolph, Thore Graepel. Conjugate Directions for Stochastic Gradient Descent 2002 Proceedings of the International Conference on Neural Networks, ICANN 2002 J.R. Dorronsoro 2415 1351--1356 Lecture Notes in Computer Science
• Thore Graepel. Kernel matrix completion by semidefinite programming 2002 Proceedings of the International Conference on Neural Networks, ICANN 2002 J.R. Dorronsoro 2415 694--699 Lecture Notes in Computer Science
• Thore Graepel. PAC-Bayesian Pattern Classification with Kernels: Theory, Algorithms, and an Application to the Game of Go 2002 Berlin, Germany Department of Computer Science, Technical Universi
• Thore Graepel, Nicol N. Schraudolph. Stable adaptive momentum for rapid online learning in nonlinear systems 2002 Proceedings of the International Conference on Neural Networks, ICANN 2002 J.R. Dorronsoro 2415 450--455 Lecture Notes in Computer Science
• Nicol N. Schraudolph, Thore Graepel. Towards Stochastic Conjugate Gradient Methods 2002 Proceedings of the 9th International Conference on Neural Information Processing, ICONIP 2002

2001

• Ralf Herbrich, Thore Graepel. A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work 2001 Advances in Neural Information Processing Systems 13 224-230
• Thore Graepel, Ralf Herbrich. A PAC-Bayesian Margin Distribution Bound for Kernel Classifiers (extended abstract) 2001
• Ralf Herbrich, Thore Graepel, Colin Campbell. Bayes Point Machines 2001 Journal of Machine Learning Research 245-279 1
• Thore Graepel, Ralf Herbrich, Robert C. Williamson. From Margin to Sparsity 2001 Advances in Neural Information Processing Systems 13 210--216
• Ralf Herbrich, Thore Graepel. Large Scale Bayes Point Machines 2001 Advances in Neural Information Processing Systems 13 528--534
• Thore Graepel, Mike Goutrie, Marco Krüger, Ralf Herbrich. Learning on Graphs in the Game of Go 2001 Proceedings of the Ninth International Conference on Artificial Neural Networks 347--352
• Thore Graepel, Ralf Herbrich. The Kernel Gibbs Sampler 2001 Advances in Neural Information Processing Systems 13 514--520

2000

• Thore Graepel, Ralf Herbrich, Klaus Obermayer. Bayesian Transduction 2000 Advances in Neural Information Processing Systems 12 456--462
• Thore Graepel, Ralf Herbrich, John Shawe-Taylor. Generalisation Error Bounds for Sparse Linear Classifiers 2000 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory 298--303
• Ralf Herbrich, Thore Graepel, Klaus Obermayer. Large Margin Rank Boundaries for Ordinal Regression 2000 Advances in Large Margin Classifiers 115--132
• Ralf Herbrich, Thore Graepel, Colin Campbell. Robust Bayes Point Machines 2000 Proceedings of ESANN 2000 49--54
• Ralf Herbrich, Thore Graepel, John Shawe-Taylor. Sparsity vs. Large Margins for Linear Classifiers 2000 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory 304--308
• Sambu Seo, Marko Wallat, Thore Graepel, Klaus Obermayer. Gaussian Process Regression: Active Data Selection and Test Point Rejection 2000 Proceedings of the International Joint Conference on Neural Networks IJCNN'2000 241--246 III

1999

• Ralf Herbrich, Thore Graepel, Colin Campbell. Bayes Point Machines: Estimating the Bayes Point in Kernel Space 1999 Proceedings of IJCAI Workshop Support Vector Machines 23--27
• Ralf Herbrich, Thore Graepel, Colin Campbell. Bayesian Learning in Reproducing Kernel Hilbert Spaces 1999 Technical University of Berlin
• Thore Graepel, Ralf Herbrich, Klaus Obermayer. Bayesian transductive classification by maximizing volume in version space 1999 Proceedings of Learning 1999 Conference
• Thore Graepel, Ralf Herbrich, Peter Bollmann--Sdorra, Klaus Obermayer. Classification on Pairwise Proximity Data 1999 Advances in Neural Information Processing Systems 11 438--444
• Thore Graepel, Ralf Herbrich, Bernhard Schölkopf, Alex Smola, Peter Bartlett, Klaus Robert-Müller, Klaus Obermayer, Robert Williamson. Classification on Proximity Data with LP--Machines 1999 Proceedings of the Ninth International Conference on Artificial Neural Networks 304--309
• Ralf Herbrich, Max Keilbach, Thore Graepel, Peter Bollmann--Sdorra, Klaus Obermayer. Neural Networks in Economics: Background, Applications, and New Developments 1999 Advances in Computational Economics 169--196 11
• Ralf Herbrich, Thore Graepel, Klaus Obermayer. Regression Models for Ordinal Data: A Machine Learning Approach 1999 Technical University of Berlin
• Ralf Herbrich, Thore Graepel, Klaus Obermayer. Support Vector Learning for Ordinal Regression 1999 Proceedings of the Ninth International Conference on Artificial Neural Networks 97--102
• Thore Graepel, Klaus Obermayer. A Self-Organizing Map for Proximity Data 1999 Neural Computation 139-155 11

1998

• Ralf Herbrich, Thore Graepel, Peter Bollmann--Sdorra, Klaus Obermayer. Learning a Preference Relation in IR 1998 Proceedings Workshop Text Categorization and Machine Learning, International Conference on Machine Learning 1998 80--84
• Ralf Herbrich, Thore Graepel, Peter Bollmann--Sdorra, Klaus Obermayer. Supervised Learning of Preference Relations 1998 Proceedings Fachgruppentreffen Maschinelles Lernen 43--47
• Matthias Burger, Thore Graepel, Klaus Obermayer. An Annealed Self-Organizing Map for Source Channel Coding 1998 Advances in Neural Information Processing Systems NIPS 10 430-436
• Thore Graepel, Klaus Obermayer. Fuzzy Topographic Kernel Clustering 1998 Proceedings of the 5th GI Workshop Fuzzy Neuro Systems '98 W. Brauer 90--97
• Thore Graepel, Matthias Burger, Klaus Obermayer. Self-Organizing Maps: Generalizations and New Optimization Techniques 1998 Neurocomputing 173-190 20
• Thore Graepel. Statistical Physics of Clustering Algorithms 1998 Berlin, Germany Department of Computer Science, Technical Universi