Travelled to:1 × China
1 × Ireland
1 × Israel
1 × Italy
1 × The Netherlands
3 × United Kingdom
6 × USA
Collaborated with:R.Herbrich D.H.Stern ∅ A.D.Gordon C.V.Russo J.Borgström J.Guiver G.Kasneci T.Salimans U.Paquet N.Rolland S.Bauer S.Clark L.Rimell Y.Bachrach T.Minka J.Q.Candela T.Borchert W.Cheng S.Sanner S.Guo S.Kharazmi S.Karimi Y.Xu X.Cao A.Sellen S.Lacoste-Julien K.Palla A.Davies Z.Ghahramani M.Szymczak D.Tarlow M.Aizatulin G.Claret A.V.Nori S.K.Rajamani
Talks about:bayesian (4) model (4) learn (3) game (3) probabilist (2) knowledg (2) advertis (2) program (2) predict (2) pattern (2)
Person: Thore Graepel
DBLP: Graepel:Thore
Contributed to:
2015
2014
2014
20132013
2012
2012
2011
201120102008200720062003Wrote 15 papers:
- ESOP-2015-GordonRSBRGT #probability #query #source code #spreadsheet
- Probabilistic Programs as Spreadsheet Queries (ADG, CVR, MS, JB, NR, TG, DT), pp. 1–25.
- ECIR-2014-BauerCRG #corpus #formal method #learning #web
- Learning a Theory of Marriage (and Other Relations) from a Web Corpus (SB, SC, LR, TG), pp. 591–597.
- POPL-2014-GordonGRRBG #named #probability #programming language
- Tabular: a schema-driven probabilistic programming language (ADG, TG, NR, CVR, JB, JG), pp. 321–334.
- KDD-2013-Lacoste-JulienPDKGG #knowledge base #named #scalability
- SIGMa: simple greedy matching for aligning large knowledge bases (SLJ, KP, AD, GK, TG, ZG), pp. 572–580.
- POPL-2013-GordonABCGNRR #reasoning
- A model-learner pattern for bayesian reasoning (ADG, MA, JB, GC, TG, AVN, SKR, CVR), pp. 403–416.
- ICML-2012-BachrachGMG #adaptation #crowdsourcing #how #testing #visual notation
- How To Grade a Test Without Knowing the Answers — A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing (YB, TG, TM, JG), p. 108.
- RecSys-2012-SalimansPG #collaboration #learning #ranking
- Collaborative learning of preference rankings (TS, UP, TG), pp. 261–264.
- CIKM-2011-ChengKGSH #automation #generative
- Automated feature generation from structured knowledge (WC, GK, TG, DHS, RH), pp. 1395–1404.
- CIKM-2011-SannerGGKK #optimisation #retrieval #topic
- Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model (SS, SG, TG, SK, SK), pp. 1977–1980.
- 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-2003-Graepel #difference #equation #linear #process
- Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations (TG), pp. 234–241.