# All Publications

The following listing contains all publications of the current members of the Artificial Intelligence and Sustainability group.

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

- De-Layering Social Networks by Shared Tastes of Friendships. Dietz, Laura; Gamari, Ben; Guiver, John; Snelson, Edward; Herbrich, Ralf (2012).
- Transparent User Models for Personalization. El-Arini, Khalid; Paquet, Ulrich; Herbrich, Ralf; Van Gael, Jurgen; Agüera y Arcas, Blaise (2012). 678–686.
*A Bayesian Treatment of Social Links in Recommender Systems*Gartrell, Mike; Paquet, Ulrich; Herbrich, Ralf (2012).- 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.

2011

- Automated Feature Generation From Structured Knowledge. Cheng, Weiwei; Kasneci, Gjergji; Graepel, Thore; Stern, David H; Herbrich, Ralf (2011). 1395–1404.
- 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).
- A Penny for Your Thoughts? The Value of Information in Recommendation Systems. Passos, Alexandre; Van Gael, Juergen; Herbrich, Ralf; Paquet, Ulrich (2011). 9–14.
- 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.

2010

- Fingerprinting Ratings for Collaborative Filtering - Theoretical and Empirical Analysis. Bachrach, Yoram; Herbrich, Ralf (2010). 25–36.
- 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.
- Bayesian Knowledge Corroboration with Logical Rules and User Feedback. Kasneci, Gjergji; Gael, Jurgen Van; Herbrich, Ralf; Graepel, Thore (2010). 1–18.
- Vuvuzelas & Active Learning for Online Classification. Paquet, Ulrich; Van Gael, Jurgen; Stern, David; Kasneci, Gjergji; Herbrich, Ralf; Graepel, Thore (2010).
- Collaborative Expert Portfolio Management. Stern, David; Samulowitz, Horst; Herbrich, Ralf; Graepel, Thore; Pulina, Luca; Tacchella, Armando (2010).
- Predicting Information Spreading in Twitter. Zaman, Tauhid R; Herbrich, Ralf; Van Gael, Jurgen; Stern, David (2010).
- Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. Zhang, Xinhua; Graepel, Thore; Herbrich, Ralf (2010). 956–963.

2009

- Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems. Bachrach, Yoram; Herbrich, Ralf; Porat, Ely (2009). 344–352.
- 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. - Scalable Clustering and Keyword Suggestion for Online Advertisements. Schwaighofer, Anton; Candela, Joaquin Qui nonero; Borchert, Thomas; Graepel, Thore; Herbrich, Ralf (2009). 27–36.
- 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

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

2005

- 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. - 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. - 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.
*On Gaussian Expectation Propagation*Herbrich, Ralf (2005).*Minimising the Kullback-Leibler Divergence*Herbrich, Ralf (2005).- The Structure of Version Space. Herbrich, Ralf; Graepel, Thore; Williamson, Robert C in
*Innovations in Machine Learning: Theory and Applications*(2005). 257–274.

2004

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

2003

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

2002

*Learning Kernel Classifiers: Theory and Algorithms*Herbrich, Ralf (2002). (2nd edition ) The MIT Press.[ BibTeX ]- A PAC-Bayesian Margin Bound for Linear Classifiers. Herbrich, Ralf; Graepel, Thore in
*IEEE Transactions on Information Theory*(2002).**48**(12) 3140–3150. - Algorithmic Luckiness. Herbrich, Ralf; Williamson, Robert C in
*Journal of Machine Learning Research*(2002).**3**175–212. - Learning and Generalization: Theoretical Bounds. Herbrich, Ralf; Williamson, Robert C in
*Handbook of Brain Theory and Neural Networks*(2002). (2nd edition ) 619–623. - Average Precision and the Problem of Generalisation. Hill, Simon; Zaragoza, Hugo; Herbrich, Ralf; Rayner, Peter (2002).
- Fast Sparse Gaussian Process Methods: The Informative Vector Machine. Lawrence, Neil; Seeger, Matthias; Herbrich, Ralf (2002). 609–616.
- 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.

2001

- Learning on Graphs in the Game of Go. Graepel, Thore; Goutrié, Mike; Krüger, Marco; Herbrich, Ralf (2001). 347–352.
- Support Vector Regression for Black-Box System Identification. Gretton, Arthur; Doucet, Arnaud; Herbrich, Ralf; Rayner, Peter; Schölkopf, Bernhard (2001). 341–344.
- 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.
- 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.
*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.
- A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. Herbrich, Ralf; Graepel, Thore (2000). 224–230.
- Robust Bayes Point Machines. Herbrich, Ralf; Graepel, Thore; Campbell, Colin (2000). 49–54.
- Sparsity vs. Large Margins for Linear Classifiers. Herbrich, Ralf; Graepel, Thore; Shawe-Taylor, John (2000). 304–308.

1999

- Bayesian Transduction. Graepel, Thore; Herbrich, Ralf; Obermayer, Klaus (1999). 456–462.
- 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.
- Bayes Point Machines : Estimating the Bayes Point in Kernel Space. Herbrich, Ralf; Graepel, Thore; Campbell, Colin (1999). 23–27.
- Support Vector Learning for Ordinal Regression. Herbrich, Ralf; Graepel, Thore; Obermayer, Klaus (1999). 97–102.
- Large Margin Rank Boundaries for Ordinal Regression. Herbrich, Ralf; Graepel, Thore; Obermayer, Klause in
*Advances in Large Margin Classifiers*(1999). 115–132. - 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. - 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

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

1997

*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.
- Unbiased Assesment of Learning Algorithms. Scheffer, Tobias; Herbrich, Ralf (1997). 798–803.

1996