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We introduce a new variational inference objective for hierarchical Dirichlet process admixture models. Our approach provides novel and scalable algorithms for learning nonparametric topic models of text documents and Gaussian admixture models of image patches. Improving on the point estimates of topic probabilities used in previous work, we define full variational posteriors for all latent variables and optimize parameters via a novel surrogate likelihood bound. We show that this approach hasdblp:conf/aistats/HughesKS15 fatcat:ab3kwtvtg5agjoczlzht7o6nfa