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2020 – today
- 2020
[j2]Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert:
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. J. Mach. Learn. Res. 21: 17:1-17:53 (2020)- [c26]Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho:
On the Discrepancy between Density Estimation and Sequence Generation. SPNLP@EMNLP 2020: 84-94 - [c25]Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the role of model uncertainty for electronic health records. CHIL 2020: 204-213 - [c24]Yeming Wen, Dustin Tran, Jimmy Ba:
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. ICLR 2020 - [c23]Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML 2020: 2782-2792 - [c22]Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS 2020 - [c21]Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton:
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. NeurIPS 2020 - [c20]Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier:
Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG. RecSys 2020: 591-593 - [i28]Yeming Wen, Dustin Tran, Jimmy Ba:
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning. CoRR abs/2002.06715 (2020) - [i27]Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho:
On the Discrepancy between Density Estimation and Sequence Generation. CoRR abs/2002.07233 (2020) - [i26]Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR abs/2005.07186 (2020) - [i25]Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. CoRR abs/2006.10108 (2020) - [i24]Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton:
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. CoRR abs/2006.13570 (2020) - [i23]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran:
Training independent subnetworks for robust prediction. CoRR abs/2010.06610 (2020) - [i22]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation can Harm your Calibration. CoRR abs/2010.09875 (2020)
2010 – 2019
- 2019
- [c19]Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran:
Measuring Calibration in Deep Learning. CVPR Workshops 2019: 38-41 - [c18]Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole:
Discrete Flows: Invertible Generative Models of Discrete Data. DGS@ICLR 2019 - [c17]Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner:
Bayesian Layers: A Module for Neural Network Uncertainty. NeurIPS 2019: 14633-14645 - [c16]Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole:
Discrete Flows: Invertible Generative Models of Discrete Data. NeurIPS 2019: 14692-14701 - [c15]Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson:
Noise Contrastive Priors for Functional Uncertainty. UAI 2019: 905-914 - [i21]Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran:
Measuring Calibration in Deep Learning. CoRR abs/1904.01685 (2019) - [i20]Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole:
Discrete Flows: Invertible Generative Models of Discrete Data. CoRR abs/1905.10347 (2019) - [i19]Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine A. Heller, Andrew M. Dai:
Analyzing the Role of Model Uncertainty for Electronic Health Records. CoRR abs/1906.03842 (2019) - 2018
- [c14]Dustin Tran, David M. Blei:
Implicit Causal Models for Genome-wide Association Studies. ICLR (Poster) 2018 - [c13]Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse:
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. ICLR (Poster) 2018 - [c12]Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran:
Image Transformer. ICML 2018: 4052-4061 - [c11]Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul:
Simple, Distributed, and Accelerated Probabilistic Programming. NeurIPS 2018: 7609-7620 - [c10]Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake A. Hechtman:
Mesh-TensorFlow: Deep Learning for Supercomputers. NeurIPS 2018: 10435-10444 - [i18]Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse:
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. CoRR abs/1803.04386 (2018) - [i17]Danijar Hafner, Dustin Tran, Alex Irpan, Timothy P. Lillicrap, James Davidson:
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors. CoRR abs/1807.09289 (2018) - [i16]Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake A. Hechtman:
Mesh-TensorFlow: Deep Learning for Supercomputers. CoRR abs/1811.02084 (2018) - [i15]Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew J. Johnson, Rif A. Saurous:
Simple, Distributed, and Accelerated Probabilistic Programming. CoRR abs/1811.02091 (2018) - [i14]Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran:
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. CoRR abs/1811.11926 (2018) - [i13]Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner:
Bayesian Layers: A Module for Neural Network Uncertainty. CoRR abs/1812.03973 (2018) - 2017
- [j1]Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Differentiation Variational Inference. J. Mach. Learn. Res. 18: 14:1-14:45 (2017) - [c9]Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei:
Deep Probabilistic Programming. ICLR (Poster) 2017 - [c8]Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei:
Variational Inference via \chi Upper Bound Minimization. NIPS 2017: 2732-2741 - [c7]Dustin Tran, Rajesh Ranganath, David M. Blei:
Hierarchical Implicit Models and Likelihood-Free Variational Inference. NIPS 2017: 5523-5533 - [i12]Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei:
Deep Probabilistic Programming. CoRR abs/1701.03757 (2017) - [i11]Dustin Tran, Rajesh Ranganath, David M. Blei:
Deep and Hierarchical Implicit Models. CoRR abs/1702.08896 (2017) - [i10]Dustin Tran, David M. Blei:
Implicit Causal Models for Genome-wide Association Studies. CoRR abs/1710.10742 (2017) - [i9]Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous:
TensorFlow Distributions. CoRR abs/1711.10604 (2017) - 2016
- [c6]Panos Toulis, Dustin Tran, Edoardo M. Airoldi:
Towards Stability and Optimality in Stochastic Gradient Descent. AISTATS 2016: 1290-1298 - [c5]Dustin Tran, Minjae Kim, Finale Doshi-Velez:
Spectral M-estimation with Applications to Hidden Markov Models. AISTATS 2016: 1421-1430 - [c4]Rajesh Ranganath, Dustin Tran, David M. Blei:
Hierarchical Variational Models. ICML 2016: 324-333 - [c3]Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei:
Operator Variational Inference. NIPS 2016: 496-504 - [c2]Dustin Tran, Rajesh Ranganath, David M. Blei:
Variational Gaussian Process. ICLR 2016 - [i8]Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Differentiation Variational Inference. CoRR abs/1603.00788 (2016) - [i7]Dustin Tran, Minjae Kim, Finale Doshi-Velez:
Spectral M-estimation with Applications to Hidden Markov Models. CoRR abs/1603.08815 (2016) - [i6]Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei:
Operator Variational Inference. CoRR abs/1610.09033 (2016) - [i5]Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja R. Rudolph, Dawen Liang, David M. Blei:
Edward: A library for probabilistic modeling, inference, and criticism. CoRR abs/1610.09787 (2016) - [i4]Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei:
The $χ$-Divergence for Approximate Inference. CoRR abs/1611.00328 (2016) - 2015
- [c1]Dustin Tran, David M. Blei, Edoardo M. Airoldi:
Copula variational inference. NIPS 2015: 3564-3572 - [i3]Panos Toulis, Dustin Tran, Edoardo M. Airoldi:
Stability and optimality in stochastic gradient descent. CoRR abs/1505.02417 (2015) - [i2]Dustin Tran, David M. Blei, Edoardo M. Airoldi:
Variational inference with copula augmentation. CoRR abs/1506.03159 (2015) - [i1]Rajesh Ranganath, Dustin Tran, David M. Blei:
Hierarchical Variational Models. CoRR abs/1511.02386 (2015)
Coauthor Index
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