76 citations · 191 across the 9 of their papers we have counts for
5 papers · 1 filter
Model Criticism for Bayesian Causal Inference
Dustin Tran, Francisco J. R. Ruiz, Susan Athey +1
The goal of causal inference is to understand the outcome of alternative courses of action. However, all causal inference requires assumptions. Such assumptions can be more influen…
The Generalized Reparameterization Gradient
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective. However, this technique does not easily apply…
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman +1
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization…
Exponential Family Embeddings
Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt +1
Word embeddings are a powerful approach for capturing semantic similarity among terms in a vocabulary. In this paper, we develop exponential family embeddings, a class of methods t…
Overdispersed Black-Box Variational Inference
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
We introduce overdispersed black-box variational inference, a method to reduce the variance of the Monte Carlo estimator of the gradient in black-box variational inference. Instead…