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Thore Graepel

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2009
38EEDavid H. Stern, Ralf Herbrich, Thore Graepel: Matchbox: large scale online bayesian recommendations. WWW 2009: 111-120
2008
37EEThore Graepel, Ralf Herbrich: Large scale data analysis and modelling in online services and advertising. KDD 2008: 2
2007
36EEDavid H. Stern, Ralf Herbrich, Thore Graepel: Learning to solve game trees. ICML 2007: 839-846
35EEPierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel: TrueSkill Through Time: Revisiting the History of Chess. NIPS 2007
2006
34EEDavid H. Stern, Ralf Herbrich, Thore Graepel: Bayesian pattern ranking for move prediction in the game of Go. ICML 2006: 873-880
33EERalf Herbrich, Tom Minka, Thore Graepel: TrueSkillTM: A Bayesian Skill Rating System. NIPS 2006: 569-576
32EEMichael H. Bowling, Johannes Fürnkranz, Thore Graepel, Ron Musick: Machine learning and games. Machine Learning 63(3): 211-215 (2006)
2005
31EEShivani 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)
30EEThore 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
29EEShivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth: A Large Deviation Bound for the Area Under the ROC Curve. NIPS 2004
28EEDavid H. Stern, Thore Graepel, David J. C. MacKay: Modelling Uncertainty in the Game of Go. NIPS 2004
2003
27EEJaz 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
25EEThore Graepel, Ralf Herbrich: Invariant Pattern Recognition by Semi-Definite Programming Machines. NIPS 2003
24EEThore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor: Semi-Definite Programming by Perceptron Learning. NIPS 2003
23EERalf Herbrich, Thore Graepel: Introduction to the Special Issue on Learning Theory. Journal of Machine Learning Research 4: 755-757 (2003)
2002
22EENicol N. Schraudolph, Thore Graepel: Conjugate Directions for Stochastic Gradient Descent. ICANN 2002: 1351-1358
21EEThore Graepel, Nicol N. Schraudolph: Stable Adaptive Momentum for Rapid Online Learning in Nonlinear Systems. ICANN 2002: 450-455
20EEThore Graepel: Kernel Matrix Completion by Semidefinite Programming. ICANN 2002: 694-699
19EENicola 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
17EEThore Graepel, Mike Goutrié, Marco Krüger, Ralf Herbrich: Learning on Graphs in the Game of Go. ICANN 2001: 347-352
16EERalf 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
12EERalf Herbrich, Thore Graepel, Colin Campbell: Robust Bayes Point Machines. ESANN 2000: 49-54
11EESambu 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
6EEThore 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
4EEThore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer: Classification on Pairwise Proximity Data. NIPS 1998: 438-444
3EEThore 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

Coauthor Index

1Shivani Agarwal [29] [31]
2Peter Bollmann-Sdorra (Peter Bollmann) [4]
3Michael H. Bowling [32]
4Matthias Burger [1] [2] [3]
5Colin Campbell [12] [16]
6Nicola Cancedda [19]
7Nicolò Cesa-Bianchi [19]
8Alex Conconi [19]
9Pierre Dangauthier [35]
10Johannes Fürnkranz [32]
11Claudio Gentile [19]
12Mike Goutrié [17]
13Cyril Goutte [19]
14Sariel Har-Peled [31]
15Ralf Herbrich [4] [6] [7] [8] [9] [10] [12] [14] [15] [16] [17] [18] [23] [24] [25] [29] [30] [31] [33] [34] [35] [36] [37] [38]
16Jaz S. Kandola [27]
17Andriy Kharechko [24]
18Marco Krüger [17]
19Yaoyong Li [19]
20David J. C. MacKay [28]
21Thomas P. Minka (Tom Minka) [33] [35]
22Ron Musick [32]
23Klaus Obermayer [1] [2] [3] [4] [5] [6] [11] [13]
24Jean-Michel Renders [19]
25Dan Roth [29] [31]
26Nicol N. Schraudolph [21] [22]
27Sambu Seo [11] [13]
28John Shawe-Taylor [14] [15] [19] [24] [27] [30]
29David H. Stern [28] [34] [36] [38]
30Alexei Vinokourov [19]
31Marko Wallat [11] [13]
32Robert C. Williamson [10]

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)