Ralf Herbrich

Affiliations:
  • Amazon Inc., Berlin
  • Facebook Inc., Menlo Park (former)
  • Microsoft Research, Cambridge (former)
  • Technical University of Berlin, Department of Computer Science (former)


According to our database1, Ralf Herbrich authored at least 58 papers between 1996 and 2020.

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Bibliography

2020
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data.
CoRR, 2020

2017
Machine Learning at Amazon.
Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017

2016
Learning Sparse Models at Scale.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

2014
Practical Lessons from Predicting Clicks on Ads at Facebook.
Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, 2014

2013
Speeding up large-scale learning with a social prior.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

2012
Kernel Topic Models.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Colonel Blotto on Facebook: the effect of social relations on strategic interaction.
Proceedings of the Web Science 2012, 2012

Distributed, real-time bayesian learning in online services.
Proceedings of the Sixth ACM Conference on Recommender Systems, 2012

Transparent user models for personalization.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

De-Layering Social Networks by Shared Tastes of Friendships.
Proceedings of the Sixth International Conference on Weblogs and Social Media, 2012

2011
Sociable killers: understanding social relationships in an online first-person shooter game.
Proceedings of the 2011 ACM Conference on Computer Supported Cooperative Work, 2011

Automated feature generation from structured knowledge.
Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011

2010
Bayesian Online Learning for Multi-label and Multi-variate Performance Measures.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Fingerprinting Ratings for Collaborative Filtering - Theoretical and Empirical Analysis.
Proceedings of the String Processing and Information Retrieval, 2010

Bayesian Knowledge Corroboration with Logical Rules and User Feedback.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Collaborative Expert Portfolio Management.
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

2009
Novel tools to streamline the conference review process: experiences from SIGKDD'09.
SIGKDD Explor., 2009

Matchbox: large scale online bayesian recommendations.
Proceedings of the 18th International Conference on World Wide Web, 2009

Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems.
Proceedings of the String Processing and Information Retrieval, 2009

Scalable clustering and keyword suggestion for online advertisements.
Proceedings of the 3rd ACM SIGKDD Workshop on Data Mining and Audience Intelligence for Advertising, 2009

2008
Large scale data analysis and modelling in online services and advertising.
Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

2007
TrueSkill Through Time: Revisiting the History of Chess.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Learning to solve game trees.
Proceedings of the Machine Learning, 2007

2006
TrueSkill<sup>TM</sup>: A Bayesian Skill Rating System.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Bayesian pattern ranking for move prediction in the game of Go.
Proceedings of the Machine Learning, 2006

2005
PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification.
Mach. Learn., 2005

Kernel Methods for Measuring Independence.
J. Mach. Learn. Res., 2005

Generalization Bounds for the Area Under the ROC Curve.
J. Mach. Learn. Res., 2005

Poisson-Networks: A Model for Structured Poisson Processes.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

Kernel Constrained Covariance for Dependence Measurement.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004
A Large Deviation Bound for the Area Under the ROC Curve.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003
Introduction to the Special Issue on Learning Theory.
J. Mach. Learn. Res., 2003

Online Bayes Point Machines.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2003

Semi-Definite Programming by Perceptron Learning.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Invariant Pattern Recognition by Semi-Definite Programming Machines.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

The kernel mutual information.
Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

2002
A PAC-Bayesian margin bound for linear classifiers.
IEEE Trans. Inf. Theory, 2002

Algorithmic Luckiness.
J. Mach. Learn. Res., 2002

Microsoft Cambridge at TREC 2002: Filtering Track.
Proceedings of The Eleventh Text REtrieval Conference, 2002

Fast Sparse Gaussian Process Methods: The Informative Vector Machine.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

The Perceptron Algorithm with Uneven Margins.
Proceedings of the Machine Learning, 2002

Learning Kernel Classifiers - Theory and Algorithms.
Adaptive computation and machine learning, MIT Press, ISBN: 978-0-262-08306-5, 2002

2001
Learning linear classifiers: theory and algorithms.
PhD thesis, 2001

Bayes Point Machines.
J. Mach. Learn. Res., 2001

Learning on Graphs in the Game of Go.
Proceedings of the Artificial Neural Networks, 2001

A Generalized Representer Theorem.
Proceedings of the Computational Learning Theory, 2001

2000
Large Scale Bayes Point Machines.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

From Margin to Sparsity.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

The Kernel Gibbs Sampler.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Robust Bayes Point Machines.
Proceedings of the ESANN 2000, 2000

Sparsity vs. Large Margins for Linear Classifiers.
Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28, 2000

Generalisation Error Bounds for Sparse Linear Classifiers.
Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28, 2000

1999
Bayesian Transduction.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1998
Classification on Pairwise Proximity Data.
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

1997
Unbiased Assesment of Learning Algorithms.
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1997

1996
Efficient Theta-Subsumption Based on Graph Algorithms.
Proceedings of the Inductive Logic Programming, 6th International Workshop, 1996


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