# Publications of Ralf Herbrich

The following listing contains all publications of Prof. Dr. Ralf Herbrich. Further publications of the research group can be found on the list of publications. Further individual listings are available externally on DBLP or Google Scholar.

We try to keep an up to date list of all our publications. If you are interested in a PDF that we have not uploaded yet, feel free to send us an email to get a copy. You can view all publications of the current members of the Artificial Intelligence and Sustainability group. For other listings, please see:

2020

2014

2013

2012

- Transparent User Models for Personalization. El-Arini, Khalid; Paquet, Ulrich; Herbrich, Ralf; Van Gael, Jurgen; Agüera y Arcas, Blaise (2012). 678–686.
- Kernel Topic Models. Hennig, Philipp; Stern, David; Herbrich, Ralf; Graepel, Thore (2012). 511–519.
- Distributed, Real-time Bayesian Learning in Online Services. Herbrich, Ralf (2012). 203–204.
- De-Layering Social Networks by Shared Tastes of Friendships. Dietz, Laura; Gamari, Ben; Guiver, John; Snelson, Edward; Herbrich, Ralf (2012).
*A Bayesian Treatment of Social Links in Recommender Systems*Gartrell, Mike; Paquet, Ulrich; Herbrich, Ralf (2012).

2011

- Sociable Killers: Understanding Social Relationships in an Online First-Person Shooter Game. Xu, Yan; Cao, Xiang; Sellen, Abigail; Herbrich, Ralf; Graepel, Thore (2011). 197–206.
- Behavioral Game Theory on Online Social Networks: Colonel Blotto is on Facebook. Kohli, Pushmeet; Bachrach, Yoram; Graepel, Thore; Smyth, Gavin; Armstrong, Michael; Stillwell, David; Kearns, Michael (2011).
- Automated Feature Generation From Structured Knowledge. Cheng, Weiwei; Kasneci, Gjergji; Graepel, Thore; Stern, David H; Herbrich, Ralf (2011). 1395–1404.
- A Penny for Your Thoughts? The Value of Information in Recommendation Systems. Passos, Alexandre; Van Gael, Juergen; Herbrich, Ralf; Paquet, Ulrich (2011). 9–14.

2010

- Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine. Graepel, Thore; Candela, Joaquin Qui nonero; Borchert, Thomas; Herbrich, Ralf (2010). 13–20.
- Vuvuzelas & Active Learning for Online Classification. Paquet, Ulrich; Van Gael, Jurgen; Stern, David; Kasneci, Gjergji; Herbrich, Ralf; Graepel, Thore (2010).
- Predicting Information Spreading in Twitter. Zaman, Tauhid R; Herbrich, Ralf; Van Gael, Jurgen; Stern, David (2010).
- Fingerprinting Ratings for Collaborative Filtering - Theoretical and Empirical Analysis. Bachrach, Yoram; Herbrich, Ralf (2010). 25–36.
- Collaborative Expert Portfolio Management. Stern, David; Samulowitz, Horst; Herbrich, Ralf; Graepel, Thore; Pulina, Luca; Tacchella, Armando (2010).
- Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. Zhang, Xinhua; Graepel, Thore; Herbrich, Ralf (2010). 956–963.
- Bayesian Knowledge Corroboration with Logical Rules and User Feedback. Kasneci, Gjergji; Gael, Jurgen Van; Herbrich, Ralf; Graepel, Thore (2010). 1–18.

2009

- Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems. Bachrach, Yoram; Herbrich, Ralf; Porat, Ely (2009). 344–352.
- Scalable Clustering and Keyword Suggestion for Online Advertisements. Schwaighofer, Anton; Candela, Joaquin Qui nonero; Borchert, Thomas; Graepel, Thore; Herbrich, Ralf (2009). 27–36.
- Novel Tools to Streamline the Conference Review Process: Experiences from SIGKDD’09. Flach, Peter; Spiegler, Sebastian; Golenia, Bruno; Price, Simon; Guiver, John; Herbrich, Ralf; Graepel, Thore; Zaki, Mohammed in
*SIGKDD Explorations*(2009).**11**(2) 63–67. - Matchbox: Large Scale Online Bayesian Recommendations. Stern, David; Herbrich, Ralf; Graepel, Thore (2009). 111–120.

2008

2007

- TrueSkill Through Time: Revisiting the History of Chess. Dangauthier, Pierre; Herbrich, Ralf; Minka, Tom; Graepel, Thore (2007). 931–938.
- Structure From Failure. Herbrich, Ralf; Graepel, Thore; Murphy, Brendan (2007).
- Learning to Solve Game Trees. Stern, David; Herbrich, Ralf; Graepel, Thore (2007). 839–846.

2006

- TrueSkill(TM): A Bayesian Skill Rating System. Herbrich, Ralf; Minka, Tom; Graepel, Thore (2006). 569–576.
- Ranking and Matchmaking. Graepel, Thore; Herbrich, Ralf in
*Game Developer Magazine*(2006). (10) - Bayesian Pattern Ranking for Move Prediction in the Game of Go. Stern, David; Herbrich, Ralf; Graepel, Thore (2006). 873–880.

2005

- The Structure of Version Space. Herbrich, Ralf; Graepel, Thore; Williamson, Robert C in
*Innovations in Machine Learning: Theory and Applications*(2005). 257–274. - PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. Graepel, Thore; Herbrich, Ralf; Shawe-Taylor, John in
*Machine Learning*(2005).**59**55–76. *On Gaussian Expectation Propagation*Herbrich, Ralf (2005).*Minimising the Kullback-Leibler Divergence*Herbrich, Ralf (2005).- Kernel Methods for Measuring Independence. Gretton, Arthur; Herbrich, Ralf; Smola, Alexander J; Bousquet, Olivier; Schölkopf, Bernhard in
*Journal of Machine Learning Research*(2005).**6**2075–2129. - Kernel Constrained Covariance for Dependence Measurement. Gretton, Arthur; Smola, Alexander; Bousquet, Olivier; Herbrich, Ralf; Belitski, Andrei; Augath, Mark; Murayama, Yusuke; Pauls, Jon; Schölkopf, Bernhard; Logothetis, Nikos (2005). 112–119.
- Generalization Bounds for the Area Under the ROC Curve. Agarwal, Shivani; Graepel, Thore; Herbrich, Ralf; Har-Peled, Sariel; Roth, Dan in
*Journal of Machine Learning Research*(2005).**6**393–425.

2004

- Poisson-Networks : A Model for Structured Poisson Processes. Rajaram, Shyamsundar; Graepel, Thore; Herbrich, Ralf (2004). 277–284.
- Learning to Fight. Graepel, Thore; Herbrich, Ralf; Gold, Julian (2004). 193–200.
- A Large Deviation Bound for the Area Under the ROC Curve. Agarwal, Shivani; Graepel, Thore; Herbrich, Ralf; Roth, Dan (2004). 9–16.

2003

- The Kernel Mutual Information. Gretton, Arthur; Herbrich, Ralf; Smola, Alexander (2003). 880–883.
- Semi-Definite Programming by Perceptron Learning. Graepel, Thore; Herbrich, Ralf; Kharechko, Andriy; Shawe-Taylor, John (2003). 457–464.
- Online Bayes Point Machines. Harrington, Edward; Herbrich, Ralf; Kivinen, Jyrki; Platt, John C; Williamson, Robert C (2003). 241–252.
- Invariant Pattern Recognition by Semidefinite Programming Machines. Graepel, Thore; Herbrich, Ralf (2003). 33–40.
- Introduction to the Special Issue on Learning Theory. Herbrich, Ralf; Graepel, Thore in
*Journal of Machine Learning Research*(2003).**4**755–757.

2002

- The Perceptron Algorithm with Uneven Margins. Li, Yaoyong; Zaragoza, Hugo; Herbrich, Ralf; Shawe-Taylor, John; Kandola, Jasvinder (2002). 379–386.
- Microsoft Cambridge at TREC 2002: Filtering Track. Robertson, Stephen E.; Walker, Stephen; Zaragoza, Hugo; Herbrich, Ralf (2002). 361–368.
*Learning Kernel Classifiers: Theory and Algorithms*Herbrich, Ralf (2002). (2nd edition ) The MIT Press.[ BibTeX ]- Learning and Generalization: Theoretical Bounds. Herbrich, Ralf; Williamson, Robert C in
*Handbook of Brain Theory and Neural Networks*(2002). (2nd edition ) 619–623. - Fast Sparse Gaussian Process Methods: The Informative Vector Machine. Lawrence, Neil; Seeger, Matthias; Herbrich, Ralf (2002). 609–616.
- Average Precision and the Problem of Generalisation. Hill, Simon; Zaragoza, Hugo; Herbrich, Ralf; Rayner, Peter (2002).
- Algorithmic Luckiness. Herbrich, Ralf; Williamson, Robert C in
*Journal of Machine Learning Research*(2002).**3**175–212. - A PAC-Bayesian Margin Bound for Linear Classifiers. Herbrich, Ralf; Graepel, Thore in
*IEEE Transactions on Information Theory*(2002).**48**(12) 3140–3150.

2001

- Support Vector Regression for Black-Box System Identification. Gretton, Arthur; Doucet, Arnaud; Herbrich, Ralf; Rayner, Peter; Schölkopf, Bernhard (2001). 341–344.
- Learning on Graphs in the Game of Go. Graepel, Thore; Goutrié, Mike; Krüger, Marco; Herbrich, Ralf (2001). 347–352.
- Bayes Point Machines. Herbrich, Ralf; Graepel, Thore; Campbell, Colin in
*Journal of Machine Learning Research*(2001).**1**245–279. - Algorithmic Luckiness. Herbrich, Ralf; Williamson, Robert C (2001). 391–397.
- A Generalized Representer Theorem. Schölkopf, Bernhard; Herbrich, Ralf; Smola, Alexander (2001). 416–426.

2000

- The Kernel Gibbs Sampler. Graepel, Thore; Herbrich, Ralf (2000). 514–520.
- Sparsity vs. Large Margins for Linear Classifiers. Herbrich, Ralf; Graepel, Thore; Shawe-Taylor, John (2000). 304–308.
- Robust Bayes Point Machines. Herbrich, Ralf; Graepel, Thore; Campbell, Colin (2000). 49–54.
*Learning Linear Classifiers - Theory and Algorithms*. Technical Report (PhD dissertation), Herbrich, Ralf (2000).[ BibTeX ]- Large Scale Bayes Point Machines. Herbrich, Ralf; Graepel, Thore (2000). 528–534.
- Generalisation Error Bounds for Sparse Linear Classifiers. Graepel, Thore; Herbrich, Ralf; Shawe-Taylor, John (2000). 298–303.
- From Margin to Sparsity. Graepel, Thore; Herbrich, Ralf; Williamson, Robert C (2000). 210–216.
- A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. Herbrich, Ralf; Graepel, Thore (2000). 224–230.

1999

- Support Vector Learning for Ordinal Regression. Herbrich, Ralf; Graepel, Thore; Obermayer, Klaus (1999). 97–102.
- Neural Networks in Economics : Background, Applications and New Developments. Herbrich, Ralf; Keilbach, Max; Bollmann-Sdorra, Peter; Obermayer, Klaus in
*Advances in Computational Economics*(1999).**11**169–196. - Large Margin Rank Boundaries for Ordinal Regression. Herbrich, Ralf; Graepel, Thore; Obermayer, Klause in
*Advances in Large Margin Classifiers*(1999). 115–132. - Classification on Proximity Data with LP-Machines. Graepel, Thore; Herbrich, Ralf; Schölkopf, Bernhard; Smola, Alex; Bartlett, Peter; Müller, Klaus Robert; Obermayer, Klaus; Williamson, Robert C (1999). 304–309.
- Bayesian Transduction. Graepel, Thore; Herbrich, Ralf; Obermayer, Klaus (1999). 456–462.
- Bayes Point Machines : Estimating the Bayes Point in Kernel Space. Herbrich, Ralf; Graepel, Thore; Campbell, Colin (1999). 23–27.
- Adaptive Margin Support Vector Machines for Classification. Herbrich, Ralf; Weston, Jason (1999). 97–102.
- Adaptive Margin Support Vector Machines. Weston, Jason; Herbrich, Ralf in
*Advances in Large Margin Classifiers*(1999). 281–296.

1998

- Learning Preference Relations for Information Retrieval. Herbrich, Ralf; Graepel, Thore; Bollmann-Sdorra, Peter; Obermayer, Klaus (1998). 80–84.
- Classification on Pairwise Proximity Data. Graepel, Thore; Herbrich, Ralf; Bollmann-Sdorra, Peter; Obermayer, Klaus (1998). 438–444.

1997

- Unbiased Assesment of Learning Algorithms. Scheffer, Tobias; Herbrich, Ralf (1997). 798–803.
*Segmentierung mit Gaborfiltern zur Induktion struktureller Klassifikatoren auf Bilddaten*. Technical Report (Master thesis), Herbrich, Ralf PhD thesis, Technical University Berlin. (1997).- Generation of Task-Specific Segmentation Procedures as a Model Selection Task. Herbrich, Ralf; Scheffer, Tobias (1997). 11–21.

1996