Travelled to:1 × Australia
1 × Ireland
1 × Israel
1 × United Kingdom
2 × China
4 × USA
Collaborated with:T.Graepel D.H.Stern D.Chakrabarti ∅ J.Q.Candela T.Borchert K.El-Arini U.Paquet J.V.Gael B.A.y.Arcas W.Cheng G.Kasneci Y.Xu X.Cao A.Sellen Y.Li H.Zaragoza J.Shawe-Taylor J.S.Kandola
Talks about:bayesian (3) scale (3) onlin (3) learn (3) game (3) advertis (2) predict (2) social (2) servic (2) search (2)
Person: Ralf Herbrich
DBLP: Herbrich:Ralf
Contributed to:
20132012
2012
2011
2011
20102008200720062002Wrote 10 papers:
- KDD-2013-ChakrabartiH #learning #scalability #social
- Speeding up large-scale learning with a social prior (DC, RH), pp. 650–658.
- KDD-2012-El-AriniPHGA #modelling #personalisation
- Transparent user models for personalization (KEA, UP, RH, JVG, BAyA), pp. 678–686.
- RecSys-2012-Herbrich #distributed #learning #online #realtime
- Distributed, real-time bayesian learning in online services (RH), pp. 203–204.
- CIKM-2011-ChengKGSH #automation #generative
- Automated feature generation from structured knowledge (WC, GK, TG, DHS, RH), pp. 1395–1404.
- CSCW-2011-XuCSHG #comprehension #game studies #online #social
- Sociable killers: understanding social relationships in an online first-person shooter game (YX, XC, AS, RH, TG), pp. 197–206.
- ICML-2010-GraepelCBH #predict
- Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine (TG, JQC, TB, RH), pp. 13–20.
- KDD-2008-GraepelH #data analysis #modelling #online #scalability
- Large scale data analysis and modelling in online services and advertising (TG, RH), p. 2.
- ICML-2007-SternHG #game studies #learning
- Learning to solve game trees (DHS, RH, TG), pp. 839–846.
- ICML-2006-SternHG #game studies #predict #ranking
- Bayesian pattern ranking for move prediction in the game of Go (DHS, RH, TG), pp. 873–880.
- ICML-2002-LiZHSK #algorithm
- The Perceptron Algorithm with Uneven Margins (YL, HZ, RH, JST, JSK), pp. 379–386.