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ORCIDThore Graepel
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Publications
- 2021
[i37]Thore Graepel, Ralf Herbrich:
A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works! CoRR abs/2109.13889 (2021)- 2012
- [c47]Pushmeet Kohli, Michael J. Kearns, Yoram Bachrach, Ralf Herbrich, David Stillwell, Thore Graepel:
Colonel Blotto on Facebook: the effect of social relations on strategic interaction. WebSci 2012: 141-150 - [c45]Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel:
Kernel Topic Models. AISTATS 2012: 511-519 - 2011
- [c42]Weiwei Cheng, Gjergji Kasneci
, Thore Graepel, David H. Stern, Ralf Herbrich:
Automated feature generation from structured knowledge. CIKM 2011: 1395-1404 - [c40]Yan Xu, Xiang Cao, Abigail Sellen, Ralf Herbrich, Thore Graepel:
Sociable killers: understanding social relationships in an online first-person shooter game. CSCW 2011: 197-206 - [i1]Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel:
Kernel Topic Models. CoRR abs/1110.4713 (2011) - 2010
- [c38]David H. Stern, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, Armando Tacchella:
Collaborative Expert Portfolio Management. AAAI 2010: 179-184 - [c37]Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich:
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. ICML 2010: 13-20 - [c36]Gjergji Kasneci, Jurgen Van Gael, Ralf Herbrich, Thore Graepel:
Bayesian Knowledge Corroboration with Logical Rules and User Feedback. ECML/PKDD (2) 2010: 1-18 - [c34]Xinhua Zhang, Thore Graepel, Ralf Herbrich:
Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. AISTATS 2010: 956-963 - 2009
- [j9]Peter A. Flach, Sebastian Spiegler, Bruno Golénia, Simon Price, John Guiver, Ralf Herbrich, Thore Graepel, Mohammed J. Zaki:
Novel tools to streamline the conference review process: experiences from SIGKDD'09. SIGKDD Explor. 11(2): 63-67 (2009) - [c33]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 - [c32]David H. Stern, Ralf Herbrich, Thore Graepel:
Matchbox: large scale online bayesian recommendations. WWW 2009: 111-120 - 2008
- [c31]Thore Graepel, Ralf Herbrich:
Large scale data analysis and modelling in online services and advertising. KDD 2008: 2 - 2007
- [c30]David H. Stern, Ralf Herbrich, Thore Graepel:
Learning to solve game trees. ICML 2007: 839-846 - [c29]Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel:
TrueSkill Through Time: Revisiting the History of Chess. NIPS 2007: 337-344 - 2006
- [c28]David H. Stern, Ralf Herbrich, Thore Graepel:
Bayesian pattern ranking for move prediction in the game of Go. ICML 2006: 873-880 - [c27]Ralf Herbrich, Tom Minka, Thore Graepel:
TrueSkillTM: A Bayesian Skill Rating System. NIPS 2006: 569-576 - 2005
- [j7]Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth:
Generalization Bounds for the Area Under the ROC Curve. J. Mach. Learn. Res. 6: 393-425 (2005) - [j6]Thore Graepel, Ralf Herbrich, John Shawe-Taylor:
PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. Mach. Learn. 59(1-2): 55-76 (2005) - [c26]Shyamsundar Rajaram, Thore Graepel, Ralf Herbrich:
Poisson-Networks: A Model for Structured Poisson Processes. AISTATS 2005: 277-284 - 2004
- [c25]Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth:
A Large Deviation Bound for the Area Under the ROC Curve. NIPS 2004: 9-16 - 2003
- [j5]Ralf Herbrich, Thore Graepel:
Introduction to the Special Issue on Learning Theory. J. Mach. Learn. Res. 4: 755-757 (2003) - [c20]Thore Graepel, Ralf Herbrich:
Invariant Pattern Recognition by Semi-Definite Programming Machines. NIPS 2003: 33-40 - [c19]Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor:
Semi-Definite Programming by Perceptron Learning. NIPS 2003: 457-464 - 2002
- [j4]Ralf Herbrich, Thore Graepel:
A PAC-Bayesian margin bound for linear classifiers. IEEE Trans. Inf. Theory 48(12): 3140-3150 (2002) - 2001
- [j3]Ralf Herbrich, Thore Graepel, Colin Campbell:
Bayes Point Machines. J. Mach. Learn. Res. 1: 245-279 (2001) - [c14]Thore Graepel, Mike Goutrié, Marco Krüger, Ralf Herbrich:
Learning on Graphs in the Game of Go. ICANN 2001: 347-352 - 2000
- [c13]Thore Graepel, Ralf Herbrich, John Shawe-Taylor:
Generalisation Error Bounds for Sparse Linear Classifiers. COLT 2000: 298-303 - [c12]Ralf Herbrich, Thore Graepel, John Shawe-Taylor:
Sparsity vs. Large Margins for Linear Classifiers. COLT 2000: 304-308 - [c10]Ralf Herbrich, Thore Graepel, Colin Campbell:
Robust Bayes Point Machines. ESANN 2000: 49-54 - [c8]Thore Graepel, Ralf Herbrich, Robert C. Williamson:
From Margin to Sparsity. NIPS 2000: 210-216 - [c7]Ralf Herbrich, Thore Graepel:
A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. NIPS 2000: 224-230 - [c6]Thore Graepel, Ralf Herbrich:
The Kernel Gibbs Sampler. NIPS 2000: 514-520 - [c5]Ralf Herbrich, Thore Graepel:
Large Scale Bayes Point Machines. NIPS 2000: 528-534 - 1999
- [c4]Thore Graepel, Ralf Herbrich, Klaus Obermayer:
Bayesian Transduction. NIPS 1999: 456-462 - 1998
- [c3]Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer:
Classification on Pairwise Proximity Data. NIPS 1998: 438-444
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