000161317 001__ 161317
000161317 005__ 20190316235005.0
000161317 037__ $$aCONF
000161317 245__ $$aSemiparametric Latent Factor Models
000161317 269__ $$a2005
000161317 260__ $$c2005
000161317 336__ $$aConference Papers
000161317 520__ $$aWe propose a semiparametric model for regression problems involving multiple response variables. The model makes use of a set of Gaussian processes that are linearly mixed to capture dependencies that may exist among the response variables. We propose an efficient approximate inference scheme for this semiparametric model whose complexity is linear in the number of training data points. We present experimental results in the domain of multi-joint robot arm dynamics.
000161317 6531_ $$aGaussian process
000161317 6531_ $$aCo-kriging
000161317 6531_ $$aMultivariate regression
000161317 6531_ $$aSemiparametric model
000161317 700__ $$aTeh, Yee-Whye
000161317 700__ $$0244691$$aSeeger, Matthias$$g208475
000161317 700__ $$aJordan, Michael
000161317 7112_ $$aArtificial Intelligence and Statistics 10
000161317 773__ $$tArtificial Intelligence and Statistics 10
000161317 8564_ $$s146230$$uhttps://infoscience.epfl.ch/record/161317/files/aistats05.pdf$$yn/a$$zn/a
000161317 909C0 $$0252343$$pLAPMAL$$xU12368
000161317 909CO $$ooai:infoscience.tind.io:161317$$pconf$$qGLOBAL_SET
000161317 917Z8 $$x208475
000161317 937__ $$aEPFL-CONF-161317
000161317 973__ $$aOTHER$$rREVIEWED$$sPUBLISHED
000161317 980__ $$aCONF