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
• Arthur Gretton, Alexander Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos Logothetis. Kernel Constrained Covariance for Dependence Measurement 2005 Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics
• 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
• Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf. Kernel Methods for Measuring Independence 2005 Journal of Machine Learning Research 2075--2129 6

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

2003

• Neil Lawrence, Matthias Seeger, Ralf Herbrich. Fast Sparse Gaussian Process Methods: The Informative Vector Machine 2003 Advances in Neural Information Processing Systems 15 625--632
• Ralf Herbrich, Thore Graepel. Introduction to the Special Issue on Learning Theory 2003 Journal of Machine Learning Research 755--757 4
• Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson. Online Bayes Point Machines 2003 Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining 241--252
• Arthur Gretton, Ralf Herbrich, Alexander Smola. The Kernel Mutual Information 2003 Proceedings of IEEE Internaltional Conference on Acoustics, Speech and Signal Processing
• Ralf Herbrich, Thomas Minka. A Multiple Testing Problem by Jaynes 2003
• Robertson, S. E., Walker, S., Zaragoza, H., Herbrich, R.. Microsoft Cambridge at TREC 2002: Filtering track 2003 The Eleventh Text REtrieval Conference, TREC 2002 Voorhees, E. M. and Harman, D. K. 439--446

2002

• Ralf Herbrich, Thore Graepel. A PAC-Bayesian Margin Bound for Linear Classifiers 2002 IEEE Transactions on Information Theory 12 3140--3150 48
• Ralf Herbrich, Robert C. Williamson. Algorithmic Luckiness 2002 Journal of Machine Learning Research 175--212 3
• Ralf Herbrich, Robert C. Williamson. Algorithmic Luckiness 2002 Advances in Neural Information Processing Systems 14 391--397
• Simon Hill, Hugo Zaragoza, Ralf Herbrich, Peter J. Rayner. Average Precision and the Problem of Generalisation 2002 Proceedings of the ACM SIGIR Workshop on Mathematical and Formal Methods in Information Retrieval
• Ralf Herbrich. Learning Kernel Classifiers: Theory and Algorithms 2002
• Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz Kandola. The Perceptron Algorithm with Uneven Margins 2002 Proceedings of the International Conference of Machine Learning 379--386
• Ralf Herbrich, Robert C. Williamson. Learning and Generalization: Theoretical Bounds 2002 Handbook of Brain Theory and Neural Networks (2nd edition) 619--623
• Stephen E. Robertson, Stephen Walker, Hugo Zaragoza, Ralf Herbrich. Microsoft Cambridge at TREC 2002: Filtering Task 2002 Proceedings of the Ninth Text Retrieval Conference (TREC-9) 361--368

2001

• Bernhard Schölkopf, Ralf Herbrich, Alexander J. Smola. A Generalized Representer Theorem 2001 Proceedings of the Fourteenth Annual Conference on Computational Learning Theory 416--426
• 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
• Neil D. Lawrence, Ralf Herbrich. A Sparse Bayesian Compression Scheme - The Informative Vector Machine (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
• Arthur Gretton, Arnaud Doucet, Ralf Herbrich, Peter J. W. Rayner, Bernhard Schölkopf. Support Vector Regression for Black-Box System Identification 2001 11th IEEE Workshop on Statistical Signal Processing
• Thore Graepel, Ralf Herbrich. The Kernel Gibbs Sampler 2001 Advances in Neural Information Processing Systems 13 514--520
• Hugo Zaragoza, Ralf Herbrich. The Perceptron meets Reuters (extended abstract) 2001
• Neil D. Lawrence, Ralf Herbrich. A Sparse Bayesian Compression Scheme - The Informative Vector Machine (extended abstract) 2001
• Hugo Zaragoza, Ralf Herbrich. The Perceptron meets Reuters (extended abstract) 2001
• Arthur Gretton, Ralf Herbrich, Olivier Chapelle, Bernhard Schölkopf, Peter J. W. Rayner. Leave-One-Out Error Estimates for \nu-SVMs (extended abstract) 2001

2000

• Bernhard Schölkopf, Ralf Herbrich, Alexander J. Smola, Robert C. Williamson. A Generalized Representer Theorem 2000 Royal Holloway, University of London
• Jason Weston, Ralf Herbrich. Adaptive Margin Support Vector Machines 2000 Advances in Large Margin Classifiers 281--296
• 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. Learning Linear Classifiers --- Theory and Algorithms 2000 Technische Universität Berlin
• 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

1999

• Ralf Herbrich, Jason Weston. Adaptive Margin Support Vector Machines for Classification Learning 1999 Proceedings of the Ninth International Conference on Artificial Neural Networks 880--885
• 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. Exact Tail Bounds for Binomial Distributed Variables 1999
• 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

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

Before 1998

• Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki. Efficient theta-subsumption based on graph algorithms 1996 Lecture Notes in Artifical Intelligence 212--228 1314
• Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki. Efficient theta-subsumption based on graph algorithms 1996 Proceedings International Workshop on Inductive Logic Programming 312--329
• Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki. Graph based subsumption algorithms for machine learning 1996 Proceedings zum Fachgruppentreffen Maschinelles Lernen 101--105
• Ralf Herbrich, Eric Heymann. Animation von flexiblen Objekte 1997 Technical University of Berlin
• Ralf Herbrich, Tobias Scheffer. Generation of task specific segmentation procedures as a model selection task 1997 Proceedings of the Visual Information Processing Workshop 11-24
• Ralf Herbrich. Segmentierung mit Gaborfiltern zur Induktion struktureller Klassifikatoren auf Bilddaten 1997 Technical University of Berlin
• Tobias Scheffer, Ralf Herbrich. Unbiased Assessment of Learning Algorithms 1997 Proceedings of the International Joint Conference on Artificial Intelligence 798--803