Stochastic Variational Inference
This enables complex Bayesian models to be applied to large-scale data, addressing scalability issues in machine learning.
The paper tackled the problem of scaling variational inference for approximating posterior distributions in probabilistic models, resulting in a stochastic algorithm that handles massive datasets like millions of articles and outperforms traditional methods.
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.