Ralf Herbrich

	2009
36		Anton 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
35		Yoram Bachrach, Ralf Herbrich, Ely Porat: Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems. SPIRE 2009: 344-352
34		David H. Stern, Ralf Herbrich, Thore Graepel: Matchbox: large scale online bayesian recommendations. WWW 2009: 111-120
	2008
33		Thore Graepel, Ralf Herbrich: Large scale data analysis and modelling in online services and advertising. KDD 2008: 2
	2007
32		David H. Stern, Ralf Herbrich, Thore Graepel: Learning to solve game trees. ICML 2007: 839-846
31		Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel: TrueSkill Through Time: Revisiting the History of Chess. NIPS 2007
	2006
30		David H. Stern, Ralf Herbrich, Thore Graepel: Bayesian pattern ranking for move prediction in the game of Go. ICML 2006: 873-880
29		Ralf Herbrich, Tom Minka, Thore Graepel: TrueSkillTM: A Bayesian Skill Rating System. NIPS 2006: 569-576
	2005
28		Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf: Kernel Methods for Measuring Independence. Journal of Machine Learning Research 6: 2075-2129 (2005)
27		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)
26		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
25		Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth: A Large Deviation Bound for the Area Under the ROC Curve. NIPS 2004
	2003
24		Thore Graepel, Ralf Herbrich: Invariant Pattern Recognition by Semi-Definite Programming Machines. NIPS 2003
23		Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor: Semi-Definite Programming by Perceptron Learning. NIPS 2003
22		Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson: Online Bayes Point Machines. PAKDD 2003: 241-252
21		Ralf 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
19		Neil D. Lawrence, Matthias Seeger, Ralf Herbrich: Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS 2002: 609-616
18		Stephen 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)
16		Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. Journal of Machine Learning Research 3: 175-212 (2002)
	2001
15		Bernhard Schölkopf, Ralf Herbrich, Alex J. Smola: A Generalized Representer Theorem. COLT/EuroCOLT 2001: 416-426
14		Thore Graepel, Mike Goutrié, Marco Krüger, Ralf Herbrich: Learning on Graphs in the Game of Go. ICANN 2001: 347-352
13		Ralf Herbrich, Robert C. Williamson: Algorithmic Luckiness. NIPS 2001: 391-397
12		Ralf 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
9		Ralf 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
4		Thore Graepel, Ralf Herbrich, Klaus Obermayer: Bayesian Transduction. NIPS 1999: 456-462
	1998
3		Thore 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

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

Colors in the list of coauthors