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Recent advances have made it feasible to apply the stochastic variationalparadigm to a collapsed representation of latent Dirichlet allocation (LDA).While the stochastic variational paradigm has successfully been applied to anuncollapsed representation of the hierarchical Dirichlet process (HDP), noattempts to apply this type of inference in a collapsed setting ofnon-parametric topic modeling have been put forward so far. In this paper weexplore such a collapsed stochastic variational Bayes inference for the HDP.The proposed online algorithm is easy to implement and accounts for theinference of hyper-parameters. First experiments show a promising improvementin predictive performance.

Practical Collapsed Stochastic Variational Inference for the HDP