618 citations · 1.9k across the 59 of their papers we have counts for
6 papers · 1 filter
The Variational Gaussian Process
Dustin Tran, Rajesh Ranganath, David M. Blei
Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variati…
Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, David M. Blei
Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. However,…
Dynamic Poisson Factorization
Laurent Charlin, Rajesh Ranganath, James McInerney +1
Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically…
The Population Posterior and Bayesian Inference on Streams
James McInerney, Rajesh Ranganath, David M. Blei
Many modern data analysis problems involve inferences from streaming data. However, streaming data is not easily amenable to the standard probabilistic modeling approaches, which a…
Correlated Random Measures
Rajesh Ranganath, David Blei
We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian n…
Automatic Variational Inference in Stan
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman +1
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes…