2009 |
38 | EE | David H. Stern,
Ralf Herbrich,
Thore Graepel:
Matchbox: large scale online bayesian recommendations.
WWW 2009: 111-120 |
2008 |
37 | EE | Thore Graepel,
Ralf Herbrich:
Large scale data analysis and modelling in online services and advertising.
KDD 2008: 2 |
2007 |
36 | EE | David H. Stern,
Ralf Herbrich,
Thore Graepel:
Learning to solve game trees.
ICML 2007: 839-846 |
35 | EE | Pierre Dangauthier,
Ralf Herbrich,
Tom Minka,
Thore Graepel:
TrueSkill Through Time: Revisiting the History of Chess.
NIPS 2007 |
2006 |
34 | EE | David H. Stern,
Ralf Herbrich,
Thore Graepel:
Bayesian pattern ranking for move prediction in the game of Go.
ICML 2006: 873-880 |
33 | EE | Ralf Herbrich,
Tom Minka,
Thore Graepel:
TrueSkillTM: A Bayesian Skill Rating System.
NIPS 2006: 569-576 |
32 | EE | Michael H. Bowling,
Johannes Fürnkranz,
Thore Graepel,
Ron Musick:
Machine learning and games.
Machine Learning 63(3): 211-215 (2006) |
2005 |
31 | EE | Shivani Agarwal,
Thore Graepel,
Ralf Herbrich,
Sariel Har-Peled,
Dan Roth:
Generalization Bounds for the Area Under the ROC Curve.
Journal of Machine Learning Research 6: 393-425 (2005) |
30 | EE | Thore Graepel,
Ralf Herbrich,
John Shawe-Taylor:
PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification.
Machine Learning 59(1-2): 55-76 (2005) |
2004 |
29 | EE | Shivani Agarwal,
Thore Graepel,
Ralf Herbrich,
Dan Roth:
A Large Deviation Bound for the Area Under the ROC Curve.
NIPS 2004 |
28 | EE | David H. Stern,
Thore Graepel,
David J. C. MacKay:
Modelling Uncertainty in the Game of Go.
NIPS 2004 |
2003 |
27 | EE | Jaz S. Kandola,
Thore Graepel,
John Shawe-Taylor:
Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming.
COLT 2003: 288-302 |
26 | | Thore Graepel:
Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations.
ICML 2003: 234-241 |
25 | EE | Thore Graepel,
Ralf Herbrich:
Invariant Pattern Recognition by Semi-Definite Programming Machines.
NIPS 2003 |
24 | EE | Thore Graepel,
Ralf Herbrich,
Andriy Kharechko,
John Shawe-Taylor:
Semi-Definite Programming by Perceptron Learning.
NIPS 2003 |
23 | EE | Ralf Herbrich,
Thore Graepel:
Introduction to the Special Issue on Learning Theory.
Journal of Machine Learning Research 4: 755-757 (2003) |
2002 |
22 | EE | Nicol N. Schraudolph,
Thore Graepel:
Conjugate Directions for Stochastic Gradient Descent.
ICANN 2002: 1351-1358 |
21 | EE | Thore Graepel,
Nicol N. Schraudolph:
Stable Adaptive Momentum for Rapid Online Learning in Nonlinear Systems.
ICANN 2002: 450-455 |
20 | EE | Thore Graepel:
Kernel Matrix Completion by Semidefinite Programming.
ICANN 2002: 694-699 |
19 | EE | Nicola Cancedda,
Cyril Goutte,
Jean-Michel Renders,
Nicolò Cesa-Bianchi,
Alex Conconi,
Yaoyong Li,
John Shawe-Taylor,
Alexei Vinokourov,
Thore Graepel,
Claudio Gentile:
Kernel Methods for Document Filtering.
TREC 2002 |
18 | | Ralf Herbrich,
Thore Graepel:
A PAC-Bayesian margin bound for linear classifiers.
IEEE Transactions on Information Theory 48(12): 3140-3150 (2002) |
2001 |
17 | EE | Thore Graepel,
Mike Goutrié,
Marco Krüger,
Ralf Herbrich:
Learning on Graphs in the Game of Go.
ICANN 2001: 347-352 |
16 | EE | Ralf Herbrich,
Thore Graepel,
Colin Campbell:
Bayes Point Machines.
Journal of Machine Learning Research 1: 245-279 (2001) |
2000 |
15 | | Thore Graepel,
Ralf Herbrich,
John Shawe-Taylor:
Generalisation Error Bounds for Sparse Linear Classifiers.
COLT 2000: 298-303 |
14 | | Ralf Herbrich,
Thore Graepel,
John Shawe-Taylor:
Sparsity vs. Large Margins for Linear Classifiers.
COLT 2000: 304-308 |
13 | | Sambu Seo,
Marko Wallat,
Thore Graepel,
Klaus Obermayer:
Gaussian Process Regression: Active Data Selection and Test Point Rejection.
DAGM-Symposium 2000: 27-34 |
12 | EE | Ralf Herbrich,
Thore Graepel,
Colin Campbell:
Robust Bayes Point Machines.
ESANN 2000: 49-54 |
11 | EE | Sambu Seo,
Marko Wallat,
Thore Graepel,
Klaus Obermayer:
Gaussian Process Regression: Active Data Selection and Test Point Rejection.
IJCNN (3) 2000: 241-246 |
10 | | Thore Graepel,
Ralf Herbrich,
Robert C. Williamson:
From Margin to Sparsity.
NIPS 2000: 210-216 |
9 | | Ralf Herbrich,
Thore Graepel:
A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work.
NIPS 2000: 224-230 |
8 | | Thore Graepel,
Ralf Herbrich:
The Kernel Gibbs Sampler.
NIPS 2000: 514-520 |
7 | | Ralf Herbrich,
Thore Graepel:
Large Scale Bayes Point Machines.
NIPS 2000: 528-534 |
1999 |
6 | EE | Thore Graepel,
Ralf Herbrich,
Klaus Obermayer:
Bayesian Transduction.
NIPS 1999: 456-462 |
5 | | Thore Graepel,
Klaus Obermayer:
A Stochastic Self-Organizing Map for Proximity Data.
Neural Computation 11(1): 139-155 (1999) |
1998 |
4 | EE | Thore Graepel,
Ralf Herbrich,
Peter Bollmann-Sdorra,
Klaus Obermayer:
Classification on Pairwise Proximity Data.
NIPS 1998: 438-444 |
3 | EE | Thore Graepel,
Matthias Burger,
Klaus Obermayer:
Self-organizing maps: Generalizations and new optimization techniques.
Neurocomputing 21(1-3): 173-190 (1998) |
1997 |
2 | | Matthias Burger,
Thore Graepel,
Klaus Obermayer:
Phase Transitions in Soft Topographic Vector Quantization.
ICANN 1997: 619-624 |
1 | | Matthias Burger,
Thore Graepel,
Klaus Obermayer:
An Annealed Self-Organizing Map for Source Channel Coding.
NIPS 1997 |