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Ralf Herbrich Vis

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*2009
36EEAnton Schwaighofer, Joaquin Quiñonero Candela, Thomas Borchert, Thore Graepel, Ralf Herbrich: Scalable clustering and keyword suggestion for online advertisements. KDD Workshop on Data Mining and Audience Intelligence for Advertising 2009: 27-36
35EEYoram Bachrach, Ralf Herbrich, Ely Porat: Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems. SPIRE 2009: 344-352
34EEDavid H. Stern, Ralf Herbrich, Thore Graepel: Matchbox: large scale online bayesian recommendations. WWW 2009: 111-120
2008
33EEThore Graepel, Ralf Herbrich: Large scale data analysis and modelling in online services and advertising. KDD 2008: 2
2007
32EEDavid H. Stern, Ralf Herbrich, Thore Graepel: Learning to solve game trees. ICML 2007: 839-846
31EEPierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel: TrueSkill Through Time: Revisiting the History of Chess. NIPS 2007
2006
30EEDavid H. Stern, Ralf Herbrich, Thore Graepel: Bayesian pattern ranking for move prediction in the game of Go. ICML 2006: 873-880
29EERalf Herbrich, Tom Minka, Thore Graepel: TrueSkillTM: A Bayesian Skill Rating System. NIPS 2006: 569-576
2005
28EEArthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf: Kernel Methods for Measuring Independence. Journal of Machine Learning Research 6: 2075-2129 (2005)
27EEShivani 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)
26EEThore 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
25EEShivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth: A Large Deviation Bound for the Area Under the ROC Curve. NIPS 2004
2003
24EEThore Graepel, Ralf Herbrich: Invariant Pattern Recognition by Semi-Definite Programming Machines. NIPS 2003
23EEThore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor: Semi-Definite Programming by Perceptron Learning. NIPS 2003
22EEEdward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson: Online Bayes Point Machines. PAKDD 2003: 241-252
21EERalf Herbrich, Thore Graepel: Introduction to the Special Issue on Learning Theory. Journal of Machine Learning Research 4: 755-757 (2003)
2002
20 Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola: The Perceptron Algorithm with Uneven Margins. ICML 2002: 379-386
19EENeil D. Lawrence, Matthias Seeger, Ralf Herbrich: Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS 2002: 609-616
18EEStephen E. Robertson, Steve Walker, Hugo Zaragoza, Ralf Herbrich: Microsoft Cambridge at TREC 2002: Filtering Track. TREC 2002
17 Ralf Herbrich, Thore Graepel: A PAC-Bayesian margin bound for linear classifiers. IEEE Transactions on Information Theory 48(12): 3140-3150 (2002)
16EERalf Herbrich, Robert C. Williamson: Algorithmic Luckiness. Journal of Machine Learning Research 3: 175-212 (2002)
2001
15EEBernhard Schölkopf, Ralf Herbrich, Alex J. Smola: A Generalized Representer Theorem. COLT/EuroCOLT 2001: 416-426
14EEThore Graepel, Mike Goutrié, Marco Krüger, Ralf Herbrich: Learning on Graphs in the Game of Go. ICANN 2001: 347-352
13EERalf Herbrich, Robert C. Williamson: Algorithmic Luckiness. NIPS 2001: 391-397
12EERalf Herbrich, Thore Graepel, Colin Campbell: Bayes Point Machines. Journal of Machine Learning Research 1: 245-279 (2001)
2000
11 Thore Graepel, Ralf Herbrich, John Shawe-Taylor: Generalisation Error Bounds for Sparse Linear Classifiers. COLT 2000: 298-303
10 Ralf Herbrich, Thore Graepel, John Shawe-Taylor: Sparsity vs. Large Margins for Linear Classifiers. COLT 2000: 304-308
9EERalf Herbrich, Thore Graepel, Colin Campbell: Robust Bayes Point Machines. ESANN 2000: 49-54
8 Thore Graepel, Ralf Herbrich, Robert C. Williamson: From Margin to Sparsity. NIPS 2000: 210-216
7 Ralf Herbrich, Thore Graepel: A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. NIPS 2000: 224-230
6 Thore Graepel, Ralf Herbrich: The Kernel Gibbs Sampler. NIPS 2000: 514-520
5 Ralf Herbrich, Thore Graepel: Large Scale Bayes Point Machines. NIPS 2000: 528-534
1999
4EEThore Graepel, Ralf Herbrich, Klaus Obermayer: Bayesian Transduction. NIPS 1999: 456-462
1998
3EEThore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer: Classification on Pairwise Proximity Data. NIPS 1998: 438-444
1997
2 Tobias Scheffer, Ralf Herbrich: Unbiased Assesment of Learning Algorithms. IJCAI (2) 1997: 798-803
1996
1 Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki: Efficient Theta-Subsumption Based on Graph Algorithms. Inductive Logic Programming Workshop 1996: 212-228

Coauthor Index

1Shivani Agarwal [25] [27]
2Yoram Bachrach [35]
3Peter Bollmann-Sdorra (Peter Bollmann) [3]
4Thomas Borchert [36]
5Olivier Bousquet [28]
6Colin Campbell [9] [12]
7Joaquin Quiñonero Candela [36]
8Pierre Dangauthier [31]
9Mike Goutrié [14]
10Thore Graepel [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [14] [17] [21] [23] [24] [25] [26] [27] [29] [30] [31] [32] [33] [34] [36]
11Arthur Gretton [28]
12Sariel Har-Peled [27]
13Edward Harrington [22]
14Jaz S. Kandola [20]
15Andriy Kharechko [23]
16Jyrki Kivinen [22]
17Marco Krüger [14]
18Neil D. Lawrence [19]
19Yaoyong Li [20]
20Thomas P. Minka (Tom Minka) [29] [31]
21Klaus Obermayer [3] [4]
22John C. Platt [22]
23Ely Porat [35]
24Stephen E. Robertson (Stephen Robertson) [18]
25Dan Roth [25] [27]
26Tobias Scheffer [1] [2]
27Bernhard Schölkopf [15] [28]
28Anton Schwaighofer [36]
29Matthias Seeger [19]
30John Shawe-Taylor [10] [11] [20] [23] [26]
31Alexander J. Smola (Alex J. Smola) [15] [28]
32David H. Stern [30] [32] [34]
33Steve Walker [18]
34Robert C. Williamson [8] [13] [16] [22]
35Fritz Wysotzki [1]
36Hugo Zaragoza [18] [20]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)