Travelled to:1 × Canada
1 × Finland
1 × France
1 × United Kingdom
2 × China
2 × USA
Collaborated with:S.Grünewälder B.Schölkopf L.Song A.J.Smola K.Chwialkowski K.Fukumizu B.K.Sriperumbudur D.Sejdinovic K.M.Borgwardt G.Lever L.Baldassarre M.Pontil J.Shawe-Taylor W.Bounliphone A.Tenenhaus M.B.Blaschko J.Peters D.Janzing K.Muandet H.Strathmann M.L.Garcia C.Andrieu X.Zhang J.Bedo S.Patterson
Talks about:kernel (5) depend (3) estim (3) test (3) embed (2) mean (2) regressor (1) metropoli (1) hypothesi (1) supervis (1)
Person: Arthur Gretton
DBLP: Gretton:Arthur
Contributed to:
20152014201420132012200920082007Wrote 12 papers:
- ICML-2015-BounliphoneGTB #consistency #dependence
- A low variance consistent test of relative dependency (WB, AG, AT, MBB), pp. 20–29.
- ICML-c1-2014-MuandetFSGS #estimation #kernel
- Kernel Mean Estimation and Stein Effect (KM, KF, BKS, AG, BS), pp. 10–18.
- ICML-c2-2014-ChwialkowskiG #independence #kernel #process #random
- A Kernel Independence Test for Random Processes (KC, AG), pp. 1422–1430.
- ICML-c2-2014-SejdinovicSGAG #adaptation #kernel
- Kernel Adaptive Metropolis-Hastings (DS, HS, MLG, CA, AG), pp. 1665–1673.
- ICML-c3-2013-GrunewalderAS
- Smooth Operators (SG, AG, JST), pp. 1184–1192.
- ICML-2012-GrunewalderLBPG #modelling
- Modelling transition dynamics in MDPs with RKHS embeddings (SG, GL, LB, MP, AG), p. 208.
- ICML-2012-GrunewalderLGBPP
- Conditional mean embeddings as regressors (SG, GL, AG, LB, SP, MP), p. 234.
- ICML-2012-SejdinovicGSF #kernel #testing #using
- Hypothesis testing using pairwise distances and associated kernels (DS, AG, BKS, KF), p. 104.
- ICML-2009-PetersJGS #detection
- Detecting the direction of causal time series (JP, DJ, AG, BS), pp. 801–808.
- ICML-2008-SongZSGS #estimation #kernel
- Tailoring density estimation via reproducing kernel moment matching (LS, XZ, AJS, AG, BS), pp. 992–999.
- ICML-2007-SongSGB #clustering #dependence
- A dependence maximization view of clustering (LS, AJS, AG, KMB), pp. 815–822.
- ICML-2007-SongSGBB #dependence #estimation #feature model
- Supervised feature selection via dependence estimation (LS, AJS, AG, KMB, JB), pp. 823–830.