International Business Machines Corporation
GENERIC DISCRIMINATIVE INFERENCE WITH GENERATIVE MODELS

Abstract:

A computer-implemented method for computing an objective function of discriminative inference with generative models with incomplete data in which some of entries are missing is provided including acquiring an incomplete set of covariates x including incomplete features {tilde over (x)} and an incomplete pattern m indicating missing entries of the incomplete features {tilde over (x)} and computing a predictive distribution p.sub..theta.(y|x) of an outcome y by using the incomplete set of covariates x and a parameter .theta., the parameter .theta. being unknown. Learning of the parameter .theta. is performed by minimizing an objective function (.theta.):=-ln p.sub..theta.(y|x)=ln p.sub..theta.({tilde over (x)}|m)-ln p.sub..theta.(y,x|m), and the objective function (.theta.) is bounded with a difference between a marginal evidence upper bound .sub.MEUBO and a joint evidence lower bound .sub.JELBO, where ln p.sub..theta.({tilde over (x)}|m).ltoreq..sub.MEUBO and ln p.sub..theta.(y,{tilde over (x)}|m).gtoreq..sub.JELBO.