Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Practical Lessons from Predicting Clicks on Ads at Facebook
This map shows the geographic impact of Ralf Herbrich's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ralf Herbrich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ralf Herbrich more than expected).
This network shows the impact of papers produced by Ralf Herbrich. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ralf Herbrich. The network helps show where Ralf Herbrich may publish in the future.
Co-authorship network of co-authors of Ralf Herbrich
This figure shows the co-authorship network connecting the top 25 collaborators of Ralf Herbrich.
A scholar is included among the top collaborators of Ralf Herbrich based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Ralf Herbrich. Ralf Herbrich is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Zhang, Xinhua, Thore Graepel, & Ralf Herbrich. (2010). Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. UCL Discovery (University College London). 9. 956–963.16 indexed citations
Herbrich, Ralf, Thore Graepel, & Thomas Brendan Murphy. (2007). Structure from failure. 10.16 indexed citations
4.
Herbrich, Ralf, et al.. (2007). TrueSkill Through Time: Revisiting the History of Chess. HAL (Le Centre pour la Communication Scientifique Directe). 20. 337–344.58 indexed citations
5.
Gretton, Arthur, Alexander J. Smola, Olivier Bousquet, et al.. (2005). Kernel Constrained Covariance for Dependence Measurement. Max Planck Institute for Plasma Physics. 112–119.24 indexed citations
6.
Agarwal, Shivani, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, & Dan Roth. (2005). Generalization Bounds for the Area Under the ROC Curve. Journal of Machine Learning Research. 6(14). 393–425.143 indexed citations
7.
Gretton, Arthur, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, & Bernhard Schölkopf. (2005). Kernel Methods for Measuring Independence. Journal of Machine Learning Research. 6(70). 2075–2129.187 indexed citations
8.
Agarwal, Shivani, Thore Graepel, Ralf Herbrich, & Dan Roth. (2004). A Large Deviation Bound for the Area Under the ROC Curve. UCL Discovery (University College London). 17. 9–16.5 indexed citations
9.
Graepel, Thore, et al.. (2003). Semi-Definite Programming by Perceptron Learning. ePrints Soton (University of Southampton). 16. 457–464.1 indexed citations
10.
Herbrich, Ralf, Neil D. Lawrence, & Matthias Seeger. (2002). Fast Sparse Gaussian Process Methods: The Informative Vector Machine. Neural Information Processing Systems. 15. 625–632.318 indexed citations
11.
Li, Yaoyong, Hugo Zaragoza, Ralf Herbrich, John Shawe‐Taylor, & Jaz Kandola. (2002). The Perceptron Algorithm with Uneven Margins. ePrints Soton (University of Southampton). 379–386.89 indexed citations
12.
Gretton, Arthur, et al.. (2001). Estimating the Leave-One-Out Error for Classification Learning with SVMs. UCL Discovery (University College London).3 indexed citations
13.
Herbrich, Ralf, et al.. (2000). Robust Bayes Point Machines. UCL Discovery (University College London). 49–54.7 indexed citations
14.
Graepel, Thore, Ralf Herbrich, & Robert C. Williamson. (2000). From Margin to Sparsity. UCL Discovery (University College London). 210–216.28 indexed citations
15.
Robertson, Stephen, Steve Walker, Hugo Zaragoza, & Ralf Herbrich. (2000). Microsoft Cambridge at TREC 2002: Filtering Track.. Text REtrieval Conference. 361–368.44 indexed citations
16.
Herbrich, Ralf & Thore Graepel. (2000). A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. UCL Discovery (University College London). 13. 224–230.33 indexed citations
17.
Herbrich, Ralf, Thore Graepel, & John Shawe‐Taylor. (2000). Sparsity vs. Large Margins for Linear Classifiers. UCL Discovery (University College London). 304–308.4 indexed citations
18.
Graepel, Thore, Ralf Herbrich, & John Shawe‐Taylor. (2000). Generalisation Error Bounds for Sparse Linear Classifiers. UCL Discovery (University College London). 298–303.36 indexed citations
19.
Graepel, Thore & Ralf Herbrich. (2000). The Kernel Gibbs Sampler. UCL Discovery (University College London). 514–520.13 indexed citations
20.
Graepel, Thore, Ralf Herbrich, & Klaus Obermayer. (1999). Bayesian Transduction. UCL Discovery (University College London). 12. 456–462.16 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.