Ralf Herbrich - RankMe

73 papers A* 15A 9B 3C 3Journal 20Unranked 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	People Can Accurately Predict Behavior of Complex Algorithms That Are Available, Compact, and Aligned. CoRR Lindsay Popowski, Helena Vasconcelos, Ignacio Javier Fernandez, Chijioke Chinaza Mgbahurike, Ralf Herbrich, Jeffrey T. Hancock, Michael S. Bernstein
2025	J	`jnl`	Approximate Message Passing for Bayesian Neural Networks. CoRR Romeo Sommerfeld, Christian Helms, Ralf Herbrich
2025	J	`jnl`	Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions. CoRR Nicolas Alder, Shivam Nitin Kajale, Milin Tunsiricharoengul, Deblina Sarkar, Ralf Herbrich
2024	J	`jnl`	AI, Climate, and Regulation: From Data Centers to the AI Act. CoRR Kai Ebert, Nicolas Alder, Ralf Herbrich, Philipp Hacker
2024	J	`jnl`	AI, Climate, and Transparency: Operationalizing and Improving the AI Act. CoRR Nicolas Alder, Kai Ebert, Ralf Herbrich, Philipp Hacker
2024	J	`jnl`	BALI: Learning Neural Networks via Bayesian Layerwise Inference. CoRR Richard Kurle, Alexej Klushyn, Ralf Herbrich
2024	A*	`conf`	Energy-Efficient Gaussian Processes Using Low-Precision Arithmetic. ICML Nicolas Alder, Ralf Herbrich
2024	A*	`conf`	Hieros: Hierarchical Imagination on Structured State Space Sequence World Models. ICML Paul Mattes, Rainer Schlosser, Ralf Herbrich
2024	J	`jnl`	Learning to Predict Usage Options of Product Reviews with LLM-Generated Labels. CoRR Leo Kohlenberg, Leonard Horns, Frederic Sadrieh, Nils Kiele, Matthis Clausen, Konstantin Ketterer, Avetis Navasardyan, Tamara Czinczoll, Gerard de Melo, Ralf Herbrich
2023	J	`jnl`	Hieros: Hierarchical Imagination on Structured State Space Sequence World Models. CoRR Paul Mattes, Rainer Schlosser, Ralf Herbrich
2022	A*	`conf`	On the detrimental effect of invariances in the likelihood for variational inference. NeurIPS Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
2022	J	`jnl`	On the detrimental effect of invariances in the likelihood for variational inference. CoRR Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
2021	J	`jnl`	A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works! CoRR Thore Graepel, Ralf Herbrich
2020	J	`jnl`	CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data. CoRR Ralf Herbrich, Rajeev Rastogi, Roland Vollgraf
2017	A	`conf`	Machine Learning at Amazon. WSDM Ralf Herbrich
2016	A*	`conf`	Learning Sparse Models at Scale. KDD Ralf Herbrich
2014	—	`conf`	Practical Lessons from Predicting Clicks on Ads at Facebook. ADKDD@KDD Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela
2013	A*	`conf`	Speeding up large-scale learning with a social prior. KDD Deepayan Chakrabarti, Ralf Herbrich
2012	—	`conf`	Colonel Blotto on Facebook: the effect of social relations on strategic interaction. WebSci Pushmeet Kohli, Michael J. Kearns, Yoram Bachrach, Ralf Herbrich, David Stillwell, Thore Graepel
2012	A	`conf`	De-Layering Social Networks by Shared Tastes of Friendships. ICWSM Laura Dietz, Ben Gamari, John Guiver, Edward Lloyd Snelson, Ralf Herbrich
2012	A	`conf`	Distributed, real-time bayesian learning in online services. RecSys Ralf Herbrich
2012	A	`conf`	Kernel Topic Models. AISTATS Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel
2012	A*	`conf`	Transparent user models for personalization. KDD Khalid El-Arini, Ulrich Paquet, Ralf Herbrich, Jurgen Van Gael, Blaise Agüera y Arcas
2011	A	`conf`	Automated feature generation from structured knowledge. CIKM Weiwei Cheng, Gjergji Kasneci, Thore Graepel, David H. Stern, Ralf Herbrich
2011	J	`jnl`	Kernel Topic Models CoRR Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel
2011	A	`conf`	Sociable killers: understanding social relationships in an online first-person shooter game. CSCW Yan Xu, Xiang Cao, Abigail Sellen, Ralf Herbrich, Thore Graepel
2010	—	`conf`	Bayesian Knowledge Corroboration with Logical Rules and User Feedback. ECML/PKDD (2) Gjergji Kasneci, Jurgen Van Gael, Ralf Herbrich, Thore Graepel
2010	A	`conf`	Bayesian Online Learning for Multi-label and Multi-variate Performance Measures. AISTATS Xinhua Zhang, Thore Graepel, Ralf Herbrich
2010	A*	`conf`	Collaborative Expert Portfolio Management. AAAI David H. Stern, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, Armando Tacchella
2010	C	`conf`	Fingerprinting Ratings for Collaborative Filtering - Theoretical and Empirical Analysis. SPIRE Yoram Bachrach, Ralf Herbrich
2010	A*	`conf`	Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine. ICML Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf Herbrich
2009	A*	`conf`	Matchbox: large scale online bayesian recommendations. WWW David H. Stern, Ralf Herbrich, Thore Graepel
2009	J	`jnl`	Novel tools to streamline the conference review process: experiences from SIGKDD'09. SIGKDD Explor. Peter A. Flach, Sebastian Spiegler, Bruno Golénia, Simon Price, John Guiver, Ralf Herbrich, Thore Graepel, Mohammed J. Zaki
2009	—	`conf`	Scalable clustering and keyword suggestion for online advertisements. KDD Workshop on Data Mining and Audience Intelligence for Advertising Anton Schwaighofer, Joaquin Quiñonero Candela, Thomas Borchert, Thore Graepel, Ralf Herbrich
2009	C	`conf`	Sketching Algorithms for Approximating Rank Correlations in Collaborative Filtering Systems. SPIRE Yoram Bachrach, Ralf Herbrich, Ely Porat
2008	A*	`conf`	Large scale data analysis and modelling in online services and advertising. KDD Thore Graepel, Ralf Herbrich
2007	A*	`conf`	Learning to solve game trees. ICML David H. Stern, Ralf Herbrich, Thore Graepel
2007	—	`conf`	TrueSkill Through Time: Revisiting the History of Chess. NIPS Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel
2006	A*	`conf`	Bayesian pattern ranking for move prediction in the game of Go. ICML David H. Stern, Ralf Herbrich, Thore Graepel
2006	—	`conf`	TrueSkill NIPS Ralf Herbrich, Tom Minka, Thore Graepel
2005	J	`jnl`	Generalization Bounds for the Area Under the ROC Curve. J. Mach. Learn. Res. Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, Dan Roth
2005	A	`conf`	Kernel Constrained Covariance for Dependence Measurement. AISTATS Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis
2005	J	`jnl`	Kernel Methods for Measuring Independence. J. Mach. Learn. Res. Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf
2005	J	`jnl`	PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. Mach. Learn. Thore Graepel, Ralf Herbrich, John Shawe-Taylor
2005	A	`conf`	Poisson-Networks: A Model for Structured Poisson Processes. AISTATS Shyamsundar Rajaram, Thore Graepel, Ralf Herbrich
2004	—	`conf`	A Large Deviation Bound for the Area Under the ROC Curve. NIPS Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth
2003	J	`jnl`	Introduction to the Special Issue on Learning Theory. J. Mach. Learn. Res. Ralf Herbrich, Thore Graepel
2003	—	`conf`	Invariant Pattern Recognition by Semi-Definite Programming Machines. NIPS Thore Graepel, Ralf Herbrich
2003	B	`conf`	Online Bayes Point Machines. PAKDD Edward Harrington, Ralf Herbrich, Jyrki Kivinen, John C. Platt, Robert C. Williamson
2003	—	`conf`	Semi-Definite Programming by Perceptron Learning. NIPS Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor
2003	—	`conf`	The kernel mutual information. ICASSP (4) Arthur Gretton, Ralf Herbrich, Alexander J. Smola
2002	J	`jnl`	A PAC-Bayesian margin bound for linear classifiers. IEEE Trans. Inf. Theory Ralf Herbrich, Thore Graepel
2002	J	`jnl`	Algorithmic Luckiness. J. Mach. Learn. Res. Ralf Herbrich, Robert C. Williamson
2002	—	`conf`	Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich
2002	—	`book`	Learning Kernel Classifiers - Theory and Algorithms. Ralf Herbrich
2002	—	`conf`	Microsoft Cambridge at TREC 2002: Filtering Track. TREC Stephen E. Robertson, Steve Walker, Hugo Zaragoza, Ralf Herbrich
2002	A*	`conf`	The Perceptron Algorithm with Uneven Margins. ICML Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola
2001	—	`conf`	A Generalized Representer Theorem. COLT/EuroCOLT Bernhard Schölkopf, Ralf Herbrich, Alexander J. Smola
2001	—	`conf`	Algorithmic Luckiness. NIPS Ralf Herbrich, Robert C. Williamson
2001	J	`jnl`	Bayes Point Machines. J. Mach. Learn. Res. Ralf Herbrich, Thore Graepel, Colin Campbell
2001	—	`—`	Learning linear classifiers: theory and algorithms. Ralf Herbrich
2001	C	`conf`	Learning on Graphs in the Game of Go. ICANN Thore Graepel, Mike Goutrié, Marco Krüger, Ralf Herbrich
2000	—	`conf`	A PAC-Bayesian Margin Bound for Linear Classifiers: Why SVMs work. NIPS Ralf Herbrich, Thore Graepel
2000	—	`conf`	From Margin to Sparsity. NIPS Thore Graepel, Ralf Herbrich, Robert C. Williamson
2000	A*	`conf`	Generalisation Error Bounds for Sparse Linear Classifiers. COLT Thore Graepel, Ralf Herbrich, John Shawe-Taylor
2000	—	`conf`	Large Scale Bayes Point Machines. NIPS Ralf Herbrich, Thore Graepel
2000	B	`conf`	Robust Bayes Point Machines. ESANN Ralf Herbrich, Thore Graepel, Colin Campbell
2000	A*	`conf`	Sparsity vs. Large Margins for Linear Classifiers. COLT Ralf Herbrich, Thore Graepel, John Shawe-Taylor
2000	—	`conf`	The Kernel Gibbs Sampler. NIPS Thore Graepel, Ralf Herbrich
1999	—	`conf`	Bayesian Transduction. NIPS Thore Graepel, Ralf Herbrich, Klaus Obermayer
1998	—	`conf`	Classification on Pairwise Proximity Data. NIPS Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer
1997	—	`conf`	Unbiased Assesment of Learning Algorithms. IJCAI (2) Tobias Scheffer, Ralf Herbrich
1996	B	`conf`	Efficient Theta-Subsumption Based on Graph Algorithms. Inductive Logic Programming Workshop Tobias Scheffer, Ralf Herbrich, Fritz Wysotzki