Dustin Tran

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 76

Top Topics

Gaussian Process

Variational Inference

Top Venues

Publications

Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le
Long-form factuality in large language models. CoRR (2024)
James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Jeremiah Zhe Liu, Xiuye Gu, Yin Cui, Dustin Tran, Balaji Lakshminarayanan
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. CoRR (2023)
James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models. ICML (2023)
Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. 24 (2023)
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. CoRR (2023)
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. ICML (2023)
Jerry W. Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, Tengyu Ma
Larger language models do in-context learning differently. CoRR (2023)
James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (2022)
Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR (2022)
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. CoRR (2022)
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR (2022)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022)
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic
Revisiting the Calibration of Modern Neural Networks. NeurIPS (2021)
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs
Soft Calibration Objectives for Neural Networks. CoRR (2021)
Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato
Sampling the Variational Posterior with Local Refinement. Entropy 23 (11) (2021)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR (2021)
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks. NeurIPS Datasets and Benchmarks (2021)
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
Combining Ensembles and Data Augmentation Can Harm Your Calibration. ICLR (2021)
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic
Revisiting the Calibration of Modern Neural Networks. CoRR (2021)
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs
Soft Calibration Objectives for Neural Networks. NeurIPS (2021)
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran
Training independent subnetworks for robust prediction. ICLR (2021)
Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems. CoRR (2021)
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR (2021)
James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton
Sparse MoEs meet Efficient Ensembles. CoRR (2021)
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 (2020)
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)
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 (2020)
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)
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 (2020)
Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho
On the Discrepancy between Density Estimation and Sequence Generation. CoRR (2020)
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)
Yeming Wen, Dustin Tran, Jimmy Ba
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning. CoRR (2020)
Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. NeurIPS (2020)
Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. CoRR (2020)
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)
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 (2020)
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 (2020)
Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho
On the Discrepancy between Density Estimation and Sequence Generation. SPNLP@EMNLP (2020)
Yeming Wen, Dustin Tran, Jimmy Ba
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning. ICLR (2020)
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 (2019)
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
Discrete Flows: Invertible Generative Models of Discrete Data. CoRR (2019)
Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran
Measuring Calibration in Deep Learning. CVPR Workshops (2019)
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
Discrete Flows: Invertible Generative Models of Discrete Data. NeurIPS (2019)
Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner
Bayesian Layers: A Module for Neural Network Uncertainty. NeurIPS (2019)
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
Discrete Flows: Invertible Generative Models of Discrete Data. DGS@ICLR (2019)
Jeremy Nixon, Mike Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran
Measuring Calibration in Deep Learning. CoRR (2019)
Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson
Noise Contrastive Priors for Functional Uncertainty. UAI (2019)
Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul
Simple, Distributed, and Accelerated Probabilistic Programming. NeurIPS (2018)
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. CoRR (2018)
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. ICLR (Poster) (2018)
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk, Danijar Hafner
Bayesian Layers: A Module for Neural Network Uncertainty. CoRR (2018)
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 (2018)
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 (2018)
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)
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran
Image Transformer. ICML (2018)
Danijar Hafner, Dustin Tran, Alex Irpan, Timothy P. Lillicrap, James Davidson
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors. CoRR (2018)
Dustin Tran, David M. Blei
Implicit Causal Models for Genome-wide Association Studies. ICLR (Poster) (2018)
Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. CoRR (2018)
Dustin Tran, David M. Blei
Implicit Causal Models for Genome-wide Association Studies. CoRR (2017)
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
Deep Probabilistic Programming. ICLR (Poster) (2017)
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 (2017)
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
Deep Probabilistic Programming. CoRR (2017)
Dustin Tran, Rajesh Ranganath, David M. Blei
Deep and Hierarchical Implicit Models. CoRR (2017)
Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
Variational Inference via \chi Upper Bound Minimization. NIPS (2017)
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
Automatic Differentiation Variational Inference. J. Mach. Learn. Res. 18 (2017)
Dustin Tran, Rajesh Ranganath, David M. Blei
Hierarchical Implicit Models and Likelihood-Free Variational Inference. NIPS (2017)
Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei
The $χ$-Divergence for Approximate Inference. CoRR (2016)
Dustin Tran, Rajesh Ranganath, David M. Blei
Variational Gaussian Process. ICLR (2016)
Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei
Operator Variational Inference. NIPS (2016)
Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei
Operator Variational Inference. CoRR (2016)
Panos Toulis, Dustin Tran, Edoardo M. Airoldi
Towards Stability and Optimality in Stochastic Gradient Descent. AISTATS (2016)
Rajesh Ranganath, Dustin Tran, David M. Blei
Hierarchical Variational Models. ICML (2016)
Dustin Tran, Minjae Kim, Finale Doshi-Velez
Spectral M-estimation with Applications to Hidden Markov Models. CoRR (2016)
Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei
Edward: A library for probabilistic modeling, inference, and criticism. CoRR (2016)
Dustin Tran, Minjae Kim, Finale Doshi-Velez
Spectral M-estimation with Applications to Hidden Markov Models. AISTATS (2016)
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei
Automatic Differentiation Variational Inference. CoRR (2016)
Dustin Tran, David M. Blei, Edoardo M. Airoldi
Variational inference with copula augmentation. CoRR (2015)
Rajesh Ranganath, Dustin Tran, David M. Blei
Hierarchical Variational Models. CoRR (2015)
Panos Toulis, Dustin Tran, Edoardo M. Airoldi
Stability and optimality in stochastic gradient descent. CoRR (2015)
Dustin Tran, David M. Blei, Edoardo M. Airoldi
Copula variational inference. NIPS (2015)