We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections
of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in …

One of the core problems of modern statistics is to approximate difficult-to-compute probability
densities. This problem is especially important in Bayesian statistics, which frames all …

… See Blei et al.for a coordinate ascent variational inference algorithm for LDA; see
Hoffman et al.for a much faster online algorithm (and open-source software) that easily …

We propose the hierarchical Dirichlet process (HDP), a nonparametric Bayesian model for
clustering problems involving multiple groups of data. Each group of data is modeled with a …

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 …

Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics,
and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP …

Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing
both a predictive model of future text and a latent topic representation of the corpus. …

Scientists need new tools to explore and browse large collections of scholarly literature.
Thanks to organizations such as JSTOR, which scan and index the original bound archives of …

We present a new approach for modeling multi-modal data sets, focusing on the specific case
of segmented images with associated text. Learning the joint distribution of image regions …

Researchers have access to large online archives of scientific articles. As a consequence,
finding relevant papers has become more difficult. Newly formed online communities of …