Efficient Theta-Subsumption Based on Graph Algorithms. - Tobias Scheffer; Ralf Herbrich; Fritz Wysotzki (Inductive Logic Programming Workshop, 1996)

Unbiased Assesment of Learning Algorithms. - Tobias Scheffer; Ralf Herbrich (IJCAI (2), 1997)

Classification on Pairwise Proximity Data. - Thore Graepel; Ralf Herbrich; Peter Bollmann-Sdorra; Klaus Obermayer (NIPS, 1998)

Bayesian Transduction. - Thore Graepel; Ralf Herbrich; Klaus Obermayer (NIPS, 1999)

Generalisation Error Bounds for Sparse Linear Classifiers. - Thore Graepel; Ralf Herbrich; John Shawe-Taylor (COLT, 2000)

Sparsity vs. Large Margins for Linear Classifiers. - Ralf Herbrich; Thore Graepel; John Shawe-Taylor (COLT, 2000)

Robust Bayes Point Machines. - Ralf Herbrich; Thore Graepel; Colin Campbell (ESANN, 2000)

The Kernel Gibbs Sampler. - Thore Graepel; Ralf Herbrich (NIPS, 2000)

From Margin to Sparsity. - Thore Graepel; Ralf Herbrich; Robert C. Williamson (NIPS, 2000)

A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. - Ralf Herbrich; Thore Graepel (NIPS, 2000)

Large Scale Bayes Point Machines. - Ralf Herbrich; Thore Graepel (NIPS, 2000)

A Generalized Representer Theorem. - Bernhard Schölkopf; Ralf Herbrich; Alexander J. Smola (COLT/EuroCOLT, 2001)

Learning on Graphs in the Game of Go. - Thore Graepel; Mike Goutrié; Marco Krüger; Ralf Herbrich (ICANN, 2001)

Bayes Point Machines. - Ralf Herbrich; Thore Graepel; Colin Campbell (J. Mach. Learn. Res., 2001)

Learning linear classifiers: theory and algorithms. - Ralf Herbrich (2001)

Learning Kernel Classifiers - Theory and Algorithms. - Ralf Herbrich (MIT Press, 2002)

The Perceptron Algorithm with Uneven Margins. - Yaoyong Li; Hugo Zaragoza; Ralf Herbrich; John Shawe-Taylor; Jaz S. Kandola (ICML, 2002)

Fast Sparse Gaussian Process Methods: The Informative Vector Machine. - Neil D. Lawrence; Matthias W. Seeger; Ralf Herbrich (NIPS, 2002)

Microsoft Cambridge at TREC 2002: Filtering Track. - Stephen E. Robertson; Steve Walker; Hugo Zaragoza; Ralf Herbrich (TREC, 2002)

Algorithmic Luckiness. - Ralf Herbrich; Robert C. Williamson (J. Mach. Learn. Res., 2002)

A PAC-Bayesian margin bound for linear classifiers. - Ralf Herbrich; Thore Graepel (IEEE Trans. Inf. Theory, 2002)

The kernel mutual information. - Arthur Gretton; Ralf Herbrich; Alexander J. Smola (ICASSP (4), 2003)

Invariant Pattern Recognition by Semi-Definite Programming Machines. - Thore Graepel; Ralf Herbrich (NIPS, 2003)

Semi-Definite Programming by Perceptron Learning. - Thore Graepel; Ralf Herbrich; Andriy Kharechko; John Shawe-Taylor (NIPS, 2003)

Online Bayes Point Machines. - Edward Harrington; Ralf Herbrich; Jyrki Kivinen; John C. Platt; Robert C. Williamson (PAKDD, 2003)

Introduction to the Special Issue on Learning Theory. - Ralf Herbrich; Thore Graepel (J. Mach. Learn. Res., 2003)

A Large Deviation Bound for the Area Under the ROC Curve. - Shivani Agarwal 001; Thore Graepel; Ralf Herbrich; Dan Roth (NIPS, 2004)

Kernel Constrained Covariance for Dependence Measurement. - Arthur Gretton; Alexander J. Smola; Olivier Bousquet; Ralf Herbrich; Andrei Belitski; Mark Augath; Yusuke Murayama; Jon Pauls; Bernhard Schölkopf; Nikos K. Logothetis (AISTATS, 2005)

Poisson-Networks: A Model for Structured Poisson Processes. - Shyamsundar Rajaram; Thore Graepel; Ralf Herbrich (AISTATS, 2005)

Generalization Bounds for the Area Under the ROC Curve. - Shivani Agarwal 001; Thore Graepel; Ralf Herbrich; Sariel Har-Peled; Dan Roth (J. Mach. Learn. Res., 2005)

Kernel Methods for Measuring Independence. - Arthur Gretton; Ralf Herbrich; Alexander J. Smola; Olivier Bousquet; Bernhard Schölkopf (J. Mach. Learn. Res., 2005)

PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. - Thore Graepel; Ralf Herbrich; John Shawe-Taylor (Mach. Learn., 2005)

Bayesian pattern ranking for move prediction in the game of Go. - David H. Stern; Ralf Herbrich; Thore Graepel (ICML, 2006)

TrueSkillTM: A Bayesian Skill Rating System. - Ralf Herbrich; Tom Minka; Thore Graepel (NIPS, 2006)

Learning to solve game trees. - David H. Stern; Ralf Herbrich; Thore Graepel (ICML, 2007)

TrueSkill Through Time: Revisiting the History of Chess. - Pierre Dangauthier; Ralf Herbrich; Tom Minka; Thore Graepel (NIPS, 2007)

Large scale data analysis and modelling in online services and advertising. - Thore Graepel; Ralf Herbrich (KDD, 2008)

Scalable clustering and keyword suggestion for online advertisements. - Anton Schwaighofer; Joaquin Quiñonero Candela; Thomas Borchert; Thore Graepel; Ralf Herbrich (KDD Workshop on Data Mining and Audience Intelligence for Advertising, 2009)

Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems. - Yoram Bachrach; Ralf Herbrich; Ely Porat (SPIRE, 2009)

Matchbox: large scale online bayesian recommendations. - David H. Stern; Ralf Herbrich; Thore Graepel (WWW, 2009)

Novel tools to streamline the conference review process: experiences from SIGKDD'09. - Peter A. Flach; Sebastian Spiegler; Bruno Golénia; Simon Price; John Guiver; Ralf Herbrich; Thore Graepel; Mohammed J. Zaki (SIGKDD Explor., 2009)

Collaborative Expert Portfolio Management. - David H. Stern; Horst Samulowitz; Ralf Herbrich; Thore Graepel; Luca Pulina; Armando Tacchella (AAAI, 2010)

Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. - Thore Graepel; Joaquin Quiñonero Candela; Thomas Borchert; Ralf Herbrich (ICML, 2010)

Bayesian Knowledge Corroboration with Logical Rules and User Feedback. - Gjergji Kasneci; Jurgen Van Gael; Ralf Herbrich; Thore Graepel (ECML/PKDD (2), 2010)

Fingerprinting Ratings for Collaborative Filtering - Theoretical and Empirical Analysis. - Yoram Bachrach; Ralf Herbrich (SPIRE, 2010)

Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. - Xinhua Zhang; Thore Graepel; Ralf Herbrich (AISTATS, 2010)

Automated feature generation from structured knowledge. - Weiwei Cheng; Gjergji Kasneci; Thore Graepel; David H. Stern; Ralf Herbrich (CIKM, 2011)

Sociable killers: understanding social relationships in an online first-person shooter game. - Yan Xu 011; Xiang Cao; Abigail Sellen; Ralf Herbrich; Thore Graepel (CSCW, 2011)

De-Layering Social Networks by Shared Tastes of Friendships. - Laura Dietz; Ben Gamari; John Guiver; Edward Lloyd Snelson; Ralf Herbrich (ICWSM, 2012)

Transparent user models for personalization. - Khalid El-Arini; Ulrich Paquet; Ralf Herbrich; Jurgen Van Gael; Blaise Agüera y Arcas (KDD, 2012)

Distributed, real-time bayesian learning in online services. - Ralf Herbrich (RecSys, 2012)

Colonel Blotto on Facebook: the effect of social relations on strategic interaction. - Pushmeet Kohli; Michael J. Kearns; Yoram Bachrach; Ralf Herbrich; David Stillwell; Thore Graepel (WebSci, 2012)

Kernel Topic Models. - Philipp Hennig; David H. Stern; Ralf Herbrich; Thore Graepel (AISTATS, 2012)

Speeding up large-scale learning with a social prior. - Deepayan Chakrabarti; Ralf Herbrich (KDD, 2013)

Practical Lessons from Predicting Clicks on Ads at Facebook. - Xinran He; Junfeng Pan; Ou Jin; Tianbing Xu; Bo Liu; Tao Xu; Yanxin Shi; Antoine Atallah; Ralf Herbrich; Stuart Bowers; Joaquin Quiñonero Candela (ADKDD@KDD, 2014)

Learning Sparse Models at Scale. - Ralf Herbrich (KDD, 2016)

Machine Learning at Amazon. - Ralf Herbrich (WSDM, 2017)

CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data. - Ralf Herbrich; Rajeev Rastogi; Roland Vollgraf (CoRR, 2020)

A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works! - Thore Graepel; Ralf Herbrich (CoRR, 2021)

On the detrimental effect of invariances in the likelihood for variational inference. - Richard Kurle; Ralf Herbrich; Tim Januschowski; Yuyang Wang 001; Jan Gasthaus (NeurIPS, 2022)

Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic. - Nicolas Alder; Ralf Herbrich (ICML, 2024)

Hieros: Hierarchical Imagination on Structured State Space Sequence World Models. - Paul Mattes; Rainer Schlosser; Ralf Herbrich (ICML, 2024)

AI, Climate, and Transparency: Operationalizing and Improving the AI Act. - Nicolas Alder; Kai Ebert; Ralf Herbrich; Philipp Hacker (CoRR, 2024)

AI, Climate, and Regulation: From Data Centers to the AI Act. - Kai Ebert; Nicolas Alder; Ralf Herbrich; Philipp Hacker (CoRR, 2024)

Learning to Predict Usage Options of Product Reviews with LLM-Generated Labels. - Leo Kohlenberg; Leonard Horns; Frederic Sadrieh; Nils Kiele; Matthis Clausen; Konstantin Ketterer; Avetis Navasardyan; Tamara Czinczoll; Gerard de Melo; Ralf Herbrich (CoRR, 2024)

BALI: Learning Neural Networks via Bayesian Layerwise Inference. - Richard Kurle; Alexej Klushyn; Ralf Herbrich (CoRR, 2024)

Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions. - Nicolas Alder; Shivam Nitin Kajale; Milin Tunsiricharoengul; Deblina Sarkar; Ralf Herbrich (CoRR, 2025)

Approximate Message Passing for Bayesian Neural Networks. - Romeo Sommerfeld; Christian Helms; Ralf Herbrich (CoRR, 2025)