blei-lab

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Online variational Bayes for latent Dirichlet allocation (LDA)

This implements topics that change over time (Dynamic Topic Models) and a model of how individual documents predict that change.

This is a C implementation of variational EM for latent Dirichlet allocation (LDA), a topic model for text or other discrete data.

Hierarchical Dirichlet processes. Topic models where the data determine the number of topics. This implements Gibbs sampling.

Collaborative modeling for recommendation. Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings.

Online inference for the Hierarchical Dirichlet Process. Fits hierarchical Dirichlet process topic models to massive data. The algorithm determines the number of topics.

Software and data for "Using Text Embeddings for Causal Inference"

This implements hierarchical latent Dirichlet allocation, a topic model that finds a hierarchy of topics. The structure of the hierarchy is determined by the data.

The pdf and LaTeX for each paper (and sometimes the code and data used to generate the figures).

Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)

Implements supervised topic models with a categorical response.

Treeffuser is an easy-to-use package for probabilistic prediction and probabilistic regression on tabular data with tree-based diffusion models.

Deep exponential families (DEFs)