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Ralf Herbrich
Person information
- affiliation: Amazon Inc., Berlin
- affiliation (former): Facebook Inc., Menlo Park
- affiliation (former): Microsoft Research, Cambridge
- affiliation (former): Technical University of Berlin, Department of Computer Science
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2020 – today
- 2024
- [c51]Nicolas Alder, Ralf Herbrich:
Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic. ICML 2024 - [c50]Paul Mattes, Rainer Schlosser, Ralf Herbrich:
Hieros: Hierarchical Imagination on Structured State Space Sequence World Models. ICML 2024 - [i8]Nicolas Alder, Kai Ebert, Ralf Herbrich, Philipp Hacker:
AI, Climate, and Transparency: Operationalizing and Improving the AI Act. CoRR abs/2409.07471 (2024) - [i7]Kai Ebert, Nicolas Alder, Ralf Herbrich, Philipp Hacker:
AI, Climate, and Regulation: From Data Centers to the AI Act. CoRR abs/2410.06681 (2024) - [i6]Leo Kohlenberg, Leonard Horns, Frederic Sadrieh, Nils Kiele, Matthis Clausen, Konstantin Ketterer, Avetis Navasardyan, Tamara Czinczoll, Gerard de Melo, Ralf Herbrich:
Learning to Predict Usage Options of Product Reviews with LLM-Generated Labels. CoRR abs/2410.12470 (2024) - 2023
- [i5]Paul Mattes, Rainer Schlosser
, Ralf Herbrich:
Hieros: Hierarchical Imagination on Structured State Space Sequence World Models. CoRR abs/2310.05167 (2023) - 2022
- [c49]Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. NeurIPS 2022 - [i4]Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. CoRR abs/2209.07157 (2022) - 2021
- [i3]Thore Graepel, Ralf Herbrich:
A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works! CoRR abs/2109.13889 (2021) - 2020
- [i2]Ralf Herbrich, Rajeev Rastogi, Roland Vollgraf:
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data. CoRR abs/2006.04942 (2020)
2010 – 2019
- 2017
- [c48]Ralf Herbrich:
Machine Learning at Amazon. WSDM 2017: 535 - 2016
- [c47]Ralf Herbrich:
Learning Sparse Models at Scale. KDD 2016: 407 - 2014
- [c46]Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela:
Practical Lessons from Predicting Clicks on Ads at Facebook. ADKDD@KDD 2014: 5:1-5:9 - 2013
- [c45]Deepayan Chakrabarti
Speeding up large-scale learning with a social prior. KDD 2013: 650-658 - 2012
- [c44]Laura Dietz, Ben Gamari, John Guiver, Edward Lloyd Snelson, Ralf Herbrich:
De-Layering Social Networks by Shared Tastes of Friendships. ICWSM 2012 - [c43]Khalid El-Arini, Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, Blaise Agüera y Arcas:
Transparent user models for personalization. KDD 2012: 678-686 - [c42]Ralf Herbrich:
Distributed, real-time bayesian learning in online services. RecSys 2012: 203-204 - [c41]Pushmeet Kohli, Michael J. Kearns, Yoram Bachrach, Ralf Herbrich, David Stillwell, Thore Graepel:
Colonel Blotto on Facebook: the effect of social relations on strategic interaction. WebSci 2012: 141-150 - [c40]Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel:
Kernel Topic Models. AISTATS 2012: 511-519 - 2011
- [c39]Weiwei Cheng, Gjergji Kasneci
Automated feature generation from structured knowledge. CIKM 2011: 1395-1404 - [c38]Yan Xu, Xiang Cao, Abigail Sellen, Ralf Herbrich, Thore Graepel:
Sociable killers: understanding social relationships in an online first-person shooter game. CSCW 2011: 197-206 - [i1]Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel:
Kernel Topic Models. CoRR abs/1110.4713 (2011) - 2010
- [c37]David H. Stern, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, Armando Tacchella:
Collaborative Expert Portfolio Management. AAAI 2010: 179-184 - [c36]Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich:
Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. ICML 2010: 13-20 - [c35]Gjergji Kasneci
Bayesian Knowledge Corroboration with Logical Rules and User Feedback. ECML/PKDD (2) 2010: 1-18 - [c34]Yoram Bachrach, Ralf Herbrich:
Fingerprinting Ratings for Collaborative Filtering - Theoretical and Empirical Analysis. SPIRE 2010: 25-36 - [c33]Xinhua Zhang, Thore Graepel, Ralf Herbrich:
Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. AISTATS 2010: 956-963
2000 – 2009
- 2009
- [j8]Peter A. Flach
Novel tools to streamline the conference review process: experiences from SIGKDD'09. SIGKDD Explor. 11(2): 63-67 (2009) - [c32]Anton Schwaighofer, Joaquin Quiñonero Candela, Thomas Borchert, Thore Graepel, Ralf Herbrich:
Scalable clustering and keyword suggestion for online advertisements. KDD Workshop on Data Mining and Audience Intelligence for Advertising 2009: 27-36 - [c31]Yoram Bachrach, Ralf Herbrich, Ely Porat:
Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems. SPIRE 2009: 344-352 - [c30]David H. Stern, Ralf Herbrich, Thore Graepel:
Matchbox: large scale online bayesian recommendations. WWW 2009: 111-120 - 2008
- [c29]Thore Graepel, Ralf Herbrich:
Large scale data analysis and modelling in online services and advertising. KDD 2008: 2 - 2007
- [c28]David H. Stern, Ralf Herbrich, Thore Graepel:
Learning to solve game trees. ICML 2007: 839-846 - [c27]Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel:
TrueSkill Through Time: Revisiting the History of Chess. NIPS 2007: 337-344 - 2006
- [c26]David H. Stern, Ralf Herbrich, Thore Graepel:
Bayesian pattern ranking for move prediction in the game of Go. ICML 2006: 873-880 - [c25]Ralf Herbrich, Tom Minka, Thore Graepel:
TrueSkillTM: A Bayesian Skill Rating System. NIPS 2006: 569-576 - 2005
- [j7]Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled
Generalization Bounds for the Area Under the ROC Curve. J. Mach. Learn. Res. 6: 393-425 (2005) - [j6]Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf:
Kernel Methods for Measuring Independence. J. Mach. Learn. Res. 6: 2075-2129 (2005) - [j5]Thore Graepel, Ralf Herbrich, John Shawe-Taylor
PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. Mach. Learn. 59(1-2): 55-76 (2005) - [c24]Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis:
Kernel Constrained Covariance for Dependence Measurement. AISTATS 2005: 112-119 - [c23]Shyamsundar Rajaram, Thore Graepel, Ralf Herbrich:
Poisson-Networks: A Model for Structured Poisson Processes. AISTATS 2005: 277-284 - 2004
- [c22]Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth:
A Large Deviation Bound for the Area Under the ROC Curve. NIPS 2004: 9-16 - 2003
- [j4]Ralf Herbrich, Thore Graepel:
Introduction to the Special Issue on Learning Theory. J. Mach. Learn. Res. 4: 755-757 (2003) - [c21]Arthur Gretton, Ralf Herbrich, Alexander J. Smola:
The kernel mutual information. ICASSP (4) 2003: 880-884 - [c20]Thore Graepel, Ralf Herbrich:
Invariant Pattern Recognition by Semi-Definite Programming Machines. NIPS 2003: 33-40 - [c19]Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor:
Semi-Definite Programming by Perceptron Learning. NIPS 2003: 457-464 - [c18]Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson:
Online Bayes Point Machines. PAKDD 2003: 241-252 - 2002
- [b2]Ralf Herbrich:
Learning Kernel Classifiers - Theory and Algorithms. Adaptive computation and machine learning, MIT Press 2002, ISBN 978-0-262-08306-5, pp. I-XX, 1-364 - [j3]Ralf Herbrich, Robert C. Williamson:
Algorithmic Luckiness. J. Mach. Learn. Res. 3: 175-212 (2002) - [j2]Ralf Herbrich, Thore Graepel:
A PAC-Bayesian margin bound for linear classifiers. IEEE Trans. Inf. Theory 48(12): 3140-3150 (2002) - [c17]Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola:
The Perceptron Algorithm with Uneven Margins. ICML 2002: 379-386 - [c16]Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich:
Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS 2002: 609-616 - [c15]Stephen E. Robertson, Steve Walker, Hugo Zaragoza, Ralf Herbrich:
Microsoft Cambridge at TREC 2002: Filtering Track. TREC 2002 - 2001
- [b1]Ralf Herbrich:
Learning linear classifiers: theory and algorithms. Technical University of Berlin, Germany, 2001, pp. 1-202 - [j1]Ralf Herbrich, Thore Graepel, Colin Campbell:
Bayes Point Machines. J. Mach. Learn. Res. 1: 245-279 (2001) - [c14]Bernhard Schölkopf, Ralf Herbrich, Alexander J. Smola:
A Generalized Representer Theorem. COLT/EuroCOLT 2001: 416-426 - [c13]Thore Graepel, Mike Goutrié, Marco Krüger, Ralf Herbrich:
Learning on Graphs in the Game of Go. ICANN 2001: 347-352 - [c12]Ralf Herbrich, Robert C. Williamson:
Algorithmic Luckiness. NIPS 2001: 391-397 - 2000
- [c11]Thore Graepel, Ralf Herbrich, John Shawe-Taylor:
Generalisation Error Bounds for Sparse Linear Classifiers. COLT 2000: 298-303 - [c10]Ralf Herbrich, Thore Graepel, John Shawe-Taylor:
Sparsity vs. Large Margins for Linear Classifiers. COLT 2000: 304-308 - [c9]Ralf Herbrich, Thore Graepel, Colin Campbell:
Robust Bayes Point Machines. ESANN 2000: 49-54 - [c8]Thore Graepel, Ralf Herbrich, Robert C. Williamson:
From Margin to Sparsity. NIPS 2000: 210-216 - [c7]Ralf Herbrich, Thore Graepel:
A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. NIPS 2000: 224-230 - [c6]Thore Graepel, Ralf Herbrich:
The Kernel Gibbs Sampler. NIPS 2000: 514-520 - [c5]Ralf Herbrich, Thore Graepel:
Large Scale Bayes Point Machines. NIPS 2000: 528-534
1990 – 1999
- 1999
- [c4]Thore Graepel, Ralf Herbrich, Klaus Obermayer:
Bayesian Transduction. NIPS 1999: 456-462 - 1998
- [c3]Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer:
Classification on Pairwise Proximity Data. NIPS 1998: 438-444 - 1997
- [c2]Tobias Scheffer, Ralf Herbrich:
Unbiased Assesment of Learning Algorithms. IJCAI (2) 1997: 798-803 - 1996
- [c1]Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki:
Efficient Theta-Subsumption Based on Graph Algorithms. Inductive Logic Programming Workshop 1996: 212-228
Coauthor Index
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last updated on 2024-11-25 22:47 CET by the dblp team
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