Hybrid Variational/Gibbs Collapsed Inference in Topic Models
This work addresses a specific bottleneck in topic modeling by offering a more efficient and accurate inference method, though it is incremental as it builds on existing techniques.
The paper tackled the problem of inference in Bayesian networks by proposing a hybrid algorithm that combines variational Bayesian inference and collapsed Gibbs sampling, which improved testset perplexity relative to variational inference at no computational cost.
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce variance. On the other hand, variational Bayesian inference is efficient and accurate for large count values but suffers from bias for small counts. We propose a hybrid algorithm that combines the best of both worlds: it samples very small counts and applies variational updates to large counts. This hybridization is shown to significantly improve testset perplexity relative to variational inference at no computational cost.