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Combining Ensembles and Data Augmentation can Harm your Calibration

Oct 19, 2020
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran


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Training independent subnetworks for robust prediction

Oct 13, 2020
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran


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Hyperparameter Ensembles for Robustness and Uncertainty Quantification

Jun 24, 2020
Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton


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Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

Jun 17, 2020
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan


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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

May 14, 2020
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

* Code available at https://github.com/google/edward2 

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BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Feb 20, 2020
Yeming Wen, Dustin Tran, Jimmy Ba

* Eighth International Conference on Learning Representations (ICLR 2020) 

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On the Discrepancy between Density Estimation and Sequence Generation

Feb 17, 2020
Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho


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BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

Feb 17, 2020
Yeming Wen, Dustin Tran, Jimmy Ba

* In International Conference on Learning Representations, 2020 

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Analyzing the Role of Model Uncertainty for Electronic Health Records

Jun 10, 2019
Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, Andrew M. Dai

* Presented at the ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning. Code to be open-sourced 

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Discrete Flows: Invertible Generative Models of Discrete Data

May 24, 2019
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole


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Measuring Calibration in Deep Learning

Apr 02, 2019
Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran


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NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport

Mar 09, 2019
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon, Ian Langmore, Dustin Tran, Srinivas Vasudevan


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Bayesian Layers: A Module for Neural Network Uncertainty

Dec 11, 2018
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner

* Presented in NeurIPS 2018 workshop Bayesian Deep Learning. Code available at https://github.com/tensorflow/tensor2tensor 

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Simple, Distributed, and Accelerated Probabilistic Programming

Nov 29, 2018
Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

* Appears in Neural Information Processing Systems, 2018. Code available at http://bit.ly/2JpFipt 

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Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language

Nov 29, 2018
Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran

* Appears in Neural Information Processing Systems, 2018. Code available at https://github.com/google-research/autoconj 

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Mesh-TensorFlow: Deep Learning for Supercomputers

Nov 05, 2018
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman


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Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

Oct 31, 2018
Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson

* 9 pages, 5 figures 

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Image Transformer

Jun 15, 2018
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran

* Appears in International Conference on Machine Learning, 2018. Code available at https://github.com/tensorflow/tensor2tensor 

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Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

Apr 02, 2018
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

* Published as a conference paper at ICLR 2018 

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Operator Variational Inference

Mar 15, 2018
Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei

* Appears in Neural Information Processing Systems, 2016 

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Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data

Mar 10, 2018
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian Robert

* Updated. 31 pages (+ appendix) 

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TensorFlow Distributions

Nov 28, 2017
Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous


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Variational Inference via $χ$-Upper Bound Minimization

Nov 12, 2017
Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David M. Blei

* Neural Information Processing Systems, 2017 

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Hierarchical Implicit Models and Likelihood-Free Variational Inference

Nov 05, 2017
Dustin Tran, Rajesh Ranganath, David M. Blei

* Appears in Neural Information Processing Systems, 2017 

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Implicit Causal Models for Genome-wide Association Studies

Oct 30, 2017
Dustin Tran, David M. Blei


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Deep Probabilistic Programming

Mar 07, 2017
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

* Appears in International Conference on Learning Representations, 2017. A companion webpage for this paper is available at http://edwardlib.org/iclr2017 

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Edward: A library for probabilistic modeling, inference, and criticism

Feb 01, 2017
Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei


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Towards stability and optimality in stochastic gradient descent

Jun 07, 2016
Panos Toulis, Dustin Tran, Edoardo M. Airoldi

* Appears in Artificial Intelligence and Statistics, 2016 

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Hierarchical Variational Models

May 30, 2016
Rajesh Ranganath, Dustin Tran, David M. Blei

* Appears in International Conference on Machine Learning, 2016 

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