Stochastic Annealing for Variational Inference
This work addresses a specific bottleneck in variational inference for researchers, but it is incremental as it builds on existing annealing methods.
The paper tackled the problem of improving local optima in variational inference for Bayesian posterior optimization by evaluating a stochastic annealing strategy, showing clear improvement on GMM and HMM models but not on LDA.
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of the approximating distribution. We investigate an annealing method for optimizing this objective with the aim of finding a better local optimal solution and compare with deterministic annealing methods and no annealing. We show that stochastic annealing can provide clear improvement on the GMM and HMM, while performance on LDA tends to favor deterministic annealing methods.