Publications (28)
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran
Automatic Differentiation Variational Inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath +2
Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal +2
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Dustin Tran, Rajesh Ranganath, David M. Blei
Deep Probabilistic Programming
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous +3
Copula variational inference
Dustin Tran, David M. Blei, Edoardo M. Airoldi
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon +3
Edward: A library for probabilistic modeling, inference, and criticism
Dustin Tran, Alp Kucukelbir, Adji B. Dieng +3
Discussion of "Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing"
Dustin Tran, David M. Blei
Convex Techniques for Model Selection
Dustin Tran
Mesh-TensorFlow: Deep Learning for Supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar +9
Model Criticism for Bayesian Causal Inference
Dustin Tran, Francisco J. R. Ruiz, Susan Athey +1
Spectral M-estimation with Applications to Hidden Markov Models
Dustin Tran, Minjae Kim, Finale Doshi-Velez
Noise Contrastive Priors for Functional Uncertainty
Danijar Hafner, Dustin Tran, Timothy Lillicrap +2
Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, David M. Blei
Towards stability and optimality in stochastic gradient descent
Panos Toulis, Dustin Tran, Edoardo M. Airoldi
Stochastic gradient descent methods for estimation with large data sets
Dustin Tran, Panos Toulis, Edoardo M. Airoldi
On the Theory of Stein Manifolds
Dustin Tran
TensorFlow Distributions
Joshua V. Dillon, Ian Langmore, Dustin Tran +7
Implicit Causal Models for Genome-wide Association Studies
Dustin Tran, David M. Blei
Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore +5
Operator Variational Inference
Rajesh Ranganath, Jaan Altosaar, Dustin Tran +1
Non-standard Symplectic Structures via Symplectic Cohomology
Dustin Tran
Variational Inference via $Ï$-Upper Bound Minimization
Adji B. Dieng, Dustin Tran, Rajesh Ranganath +2
Image Transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit +4
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Yeming Wen, Paul Vicol, Jimmy Ba +2
The Variational Gaussian Process
Dustin Tran, Rajesh Ranganath, David M. Blei
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk +1