244 citations · 2.6k across the 44 of their papers we have counts for
4 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,…
Copula variational inference
Dustin Tran, David M. Blei, Edoardo M. Airoldi
We develop a general variational inference method that preserves dependency among the latent variables. Our method uses copulas to augment the families of distributions used in mea…
Towards stability and optimality in stochastic gradient descent
Panos Toulis, Dustin Tran, Edoardo M. Airoldi
Iterative procedures for parameter estimation based on stochastic gradient descent allow the estimation to scale to massive data sets. However, in both theory and practice, they su…