  Recent advances have made it feasible to apply the stochastic variational
paradigm to a collapsed representation of latent Dirichlet allocation (LDA).
While the stochastic variational paradigm has successfully been applied to an
uncollapsed representation of the hierarchical Dirichlet process (HDP), no
attempts to apply this type of inference in a collapsed setting of
non-parametric topic modeling have been put forward so far. In this paper we
explore such a collapsed stochastic variational Bayes inference for the HDP.
The proposed online algorithm is easy to implement and accounts for the
inference of hyper-parameters. First experiments show a promising improvement
in predictive performance.
