I am a research scientist at Google and a fourth-year Ph.D. student, where I am advised by David Blei and Andrew Gelman. I am broadly interested in advancing science and intelligence, and where the ideas involve probability, programs, and/or neural nets.
I lead development of Edward, a probabilistic programming language in TensorFlow, and contribute to TensorFlow Probability. I used to be on the Stan development team. Previously, I was a Statistics Ph.D. student at Harvard before transferring to Columbia, where I worked with Edo Airoldi and also spent time at the Harvard Intelligent Probabilistic Systems group.
Recently, I have been giving the following talk:
Publications
Preprints
Some of my work is available as preprints on arXiv.
Reliable uncertainty estimates in deep neural
networks using noise contrastive priors
A prior for neural networks in data space.
Danijar Hafner, Dustin Tran, Alex Irpan,
Timothy Lillicrap, James Davidson
TensorFlow Distributions
A backend for efficient, composable manipulation of
probability distributions.
Joshua V. Dillon, Ian Langmore, Dustin
Tran, Eugene Brevdo, Srinivas Vasudevan, Dave
Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A.
Saurous
Expectation propagation as a way of life: A
framework for Bayesian inference on partitioned
data
How to distribute inference with massive data sets and how
to combine inferences from many data sets.
Andrew Gelman, Aki Vehtari, Pasi Jylänki, Tuomas Sivula,
Dustin Tran, Swupnil Sahai, Paul
Blomstedt, John P. Cunningham, David Schiminovich,
Christian Robert
Edward: A library for probabilistic modeling,
inference, and criticism
Everything and anything about probabilistic models.
Dustin Tran, Alp Kucukelbir, Adji B. Dieng,
Maja Rudolph, Dawen Liang, David M. Blei
Model criticism for Bayesian causal inference
How to validate inferences from causal models.
Dustin Tran, Francisco J. R. Ruiz, Susan
Athey, David M. Blei
Stochastic gradient descent methods for estimation with
large data sets
Fast and statistically efficient algorithms for
generalized linear models and M-estimation.
Dustin Tran, Panos Toulis, Edoardo M.
Airoldi
Journal of Statistical Software, To appear
2018
Image Transformer
An image autoregressive model using only attention.
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam
Shazeer, Alexander Ku, Dustin Tran
International Conference on Machine Learning, 2018
Implicit causal models for genome-wide association
studies
Generative models applied to causality in genomics.
Dustin Tran, David M. Blei
International Conference on Learning Representations, 2018
Flipout: Efficient pseudo-independent weight perturbations
on mini-batches
How to make weight perturbations in evolution strategies and
variational BNNs as mini-batch-friendly as activation perturbations
in dropout and batch norm.
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran,
Roger Grosse
International Conference on Learning Representations, 2018
2017
Hierarchical implicit models and likelihood-free
variational inference
Combining the idea of implicit densities with hierarchical Bayesian
modeling and deep neural networks.
Dustin Tran, Rajesh Ranganath, David M.
Blei
Neural Information Processing Systems, 2017
Variational inference via $\chi$-upper bound
minimization
Overdispersed approximations and upper bounding
the model evidence.
Adji B. Dieng, Dustin Tran, Rajesh
Ranganath, John Paisley, David M. Blei
Neural Information Processing Systems, 2017
Comment, "Fast approximate inference for
arbitrarily large semiparametric regression models via
message passing"
The role of message passing in automated inference.
Dustin Tran, David M. Blei
Journal of the American Statistical Association,
112(517):156–158, 2017
Automatic differentiation variational inference
An automated tool for black box variational inference,
available in Stan.
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath,
Andrew Gelman, David M. Blei
Journal of Machine Learning Research, 18(14):1–45, 2017
Deep probabilistic programming
How to build a language with rich compositionality for
modeling and inference.
Dustin Tran, Matthew D. Hoffman, Rif A.
Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
International Conference on Learning Representations, 2017
2016
Operator variational inference
How to formalize computational and statistical tradeoffs in variational inference.
Rajesh Ranganath, Jaan Altosaar, Dustin
Tran, and David M. Blei
Neural Information Processing Systems, 2016
Hierarchical variational models
A Bayesian formalism for constructing expressive
variational families.
Rajesh Ranganath, Dustin Tran, David M.
Blei
International Conference on Machine Learning, 2016
Spectral M-estimation with application to hidden
Markov models
Applying M-estimation for sample efficiency and robustness
in moment-based estimators.
Dustin Tran, Minjae Kim, Finale Doshi-Velez
Artificial Intelligence and Statistics, 2016
Towards stability and optimality in stochastic gradient
descent
A stochastic gradient method combining numerical stability
and statistical efficiency.
Panos Toulis, Dustin Tran, Edoardo M.
Airoldi
Artificial Intelligence and Statistics, 2016
The variational Gaussian process
A powerful variational model that can universally
approximate any posterior.
Dustin Tran, Rajesh Ranganath, David M.
Blei
International Conference on Learning Representations, 2016
2015
Copula variational inference
Posterior approximations using copulas, which find
meaningful dependence between latent variables.
Dustin Tran, David M. Blei, Edoardo M.
Airoldi
Neural Information Processing Systems, 2015
Software
TensorFlow Probability
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.
Edward
Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. (See Edward2 in TensorFlow Probability for latest developments.)
Observations
Observations provides a one line Python API for loading standard data sets in machine learning. It automates the process from downloading, extracting, loading, and preprocessing data. Observations helps keep the workflow reproducible and follow sensible standards.