We study parameter inference in large-scale latent variable models. We first
propose an unified treatment of online inference for latent variable models
from a non-canonical exponential family, and draw explicit links between
several previously proposed frequentist or Bayesian methods...
We then propose a
novel inference method for the frequentist estimation of parameters, that
adapts MCMC methods to online inference of latent variable models with the
proper use of local Gibbs sampling. Then, for latent Dirich-let allocation,we
provide an extensive set of experiments and comparisons with existing work,
where our new approach outperforms all previously proposed methods. In
particular, using Gibbs sampling for latent variable inference is superior to
variational inference in terms of test log-likelihoods. Moreover, Bayesian
inference through variational methods perform poorly, sometimes leading to
worse fits with latent variables of higher dimensionality.