World's Best Scientists 2026 revealed!

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Computer Science

D-Index
50
Citations
15386
World Ranking
5494
National Ranking
253

Overview

Ralf Herbrich is affiliated with the Hasso Plattner Institute in Germany. Their research primarily spans the field of Computer Science, with a dominant focus on Artificial Intelligence. Contributions also extend to Statistical and Nonlinear Physics, Transportation, Modeling and Simulation, and Epidemiology.

The main topics covered in Ralf Herbrich's work include:

  • Machine Learning and Algorithms
  • Neural Networks and Applications
  • Machine Learning and Data Classification
  • Complex Network Analysis Techniques
  • Opinion Dynamics and Social Influence
  • Human Mobility and Location-Based Analysis
  • COVID-19 epidemiological studies

Ralf Herbrich has published several recent papers, including:

  • "CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data," 2020, arXiv (Cornell University)
  • "De-Layering Social Networks by Shared Tastes of Friendships," 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • "On the detrimental effect of invariances in the likelihood for variational inference," 2022, arXiv (Cornell University)
  • "Approximate Message Passing for Bayesian Neural Networks," 2025, arXiv (Cornell University)
  • "A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works!," 2021, arXiv (Cornell University)

Frequent co-authors who have collaborated with Ralf Herbrich include:

  • Richard Kurle
  • Laura Dietz
  • Ben Gamari
  • John Guiver
  • Edward Snelson

Ralf Herbrich's publications frequently appear in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Harvard Data Science Review

Best Publications

  • A Generalized Representer Theorem

    Bernhard Schölkopf;Bernhard Schölkopf;Ralf Herbrich;Ralf Herbrich;Alex J. Smola

  • Large margin rank boundaries for ordinal regression

    R. Herbrich

  • Practical Lessons from Predicting Clicks on Ads at Facebook

    Xinran He;Junfeng Pan;Ou Jin;Tianbing Xu

  • Learning Kernel Classifiers: Theory and Algorithms

    Ralf Herbrich

  • TrueSkill™: A Bayesian Skill Rating System

    Ralf Herbrich;Tom Minka;Thore Graepel

  • Learning Kernel Classifiers

    Ralf Herbrich

  • Fast Sparse Gaussian Process Methods: The Informative Vector Machine

    Ralf Herbrich;Neil D. Lawrence;Matthias Seeger

  • Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine

    Thore Graepel;Joaquin Q. Candela;Thomas Borchert;Ralf Herbrich

  • Support vector learning for ordinal regression

    R. Herbrich;T. Graepel;K. Obermayer

  • Kernel Methods for Measuring Independence

    Arthur Gretton;Ralf Herbrich;Alexander Smola;Olivier Bousquet

  • Matchbox: large scale online bayesian recommendations

    David H. Stern;Ralf Herbrich;Thore Graepel

  • Predicting Information Spreading in Twitter

    Tauhid R. Zaman;Ralf Herbrich;Jurgen Van Gael;David Stern

  • Bayes point machines

    Ralf Herbrich;Thore Graepel;Colin Campbell

  • Generalization Bounds for the Area Under the ROC Curve

    Shivani Agarwal;Thore Graepel;Ralf Herbrich;Sariel Har-Peled

  • Interactive interfaces for machine learning model evaluations

    Polly Po Yee Lee;Nicolle M. Correa;Leo Parker Dirac;Aleksandr Mikhaylovich Ingerman

  • Classification on Pairwise Proximity Data

    Thore Graepel;Ralf Herbrich;Peter Bollmann-Sdorra;Klaus Obermayer

  • The Perceptron Algorithm with Uneven Margins

    Yaoyong Li;Hugo Zaragoza;Ralf Herbrich;John Shawe-Taylor

  • Stereo video for gaming

    Thore K H Graepel;Andrew Blake;Ralf Herbrich

  • TrueSkill Through Time: Revisiting the History of Chess

    Pierre Dangauthier;Ralf Herbrich;Tom Minka;Thore Graepel

  • Learning Preference Relations for Information Retrieval

    Ralf Herbrich;Thore Graepel;Peter Bollmann-Sdorra;Klaus Obermayer

  • Classification on proximity data with LP-machines

    Thore Graepel;Ralf Herbrich;Bernhard Schölkopf;Alex Smola

  • Bayes Point Machines: Estimating the Bayes Point in Kernel Space

    R Herbrich;Th Graepel;Icg Campbell

Frequent Co-Authors

Thore Graepel
Thore Graepel University College London
Robert C. Williamson
Robert C. Williamson University of Tübingen
John Shawe-Taylor
John Shawe-Taylor University College London
Klaus Obermayer
Klaus Obermayer Technical University of Berlin
Yoram Bachrach
Yoram Bachrach DeepMind (United Kingdom)
Neil D. Lawrence
Neil D. Lawrence University of Cambridge
Matthias Seeger
Matthias Seeger Amazon (Germany)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Tom Minka
Tom Minka Microsoft (United States)

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