Dustin Tran

According to our database, Dustin Tran authored at least 52 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Long-form factuality in large language models.

Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le

CoRR, 2024

2023

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness.

Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan

J. Mach. Learn. Res., 2023

Larger language models do in-context learning differently.

Jerry W. Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, Tengyu Ma

CoRR, 2023

A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models.

James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan

Proceedings of the International Conference on Machine Learning, 2023

Scaling Vision Transformers to 22 Billion Parameters.

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

Proceedings of the International Conference on Machine Learning, 2023

2022

Deep Classifiers with Label Noise Modeling and Distance Awareness.

Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Trans. Mach. Learn. Res., 2022

Sparse MoEs meet Efficient Ensembles.

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

Trans. Mach. Learn. Res., 2022

Plex: Towards Reliability using Pretrained Large Model Extensions.

Dustin Tran, Jeremiah Z. 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

CoRR, 2022

2021

Sampling the Variational Posterior with Local Refinement.

Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato

Entropy, 2021

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning.

Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. 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

CoRR, 2021

RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems.

Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier

CoRR, 2021

Revisiting the Calibration of Modern Neural Networks.

Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Soft Calibration Objectives for Neural Networks.

Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks.

Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Mike Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Combining Ensembles and Data Augmentation Can Harm Your Calibration.

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

Proceedings of the 9th International Conference on Learning Representations, 2021

Training independent subnetworks for robust prediction.

Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data.

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

J. Mach. Learn. Res., 2020

Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG.

Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

Hyperparameter Ensembles for Robustness and Uncertainty Quantification.

Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors.

Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran

Proceedings of the 37th International Conference on Machine Learning, 2020

BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning.

Yeming Wen, Dustin Tran, Jimmy Ba

Proceedings of the 8th International Conference on Learning Representations, 2020

Analyzing the role of model uncertainty for electronic health records.

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

Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

On the Discrepancy between Density Estimation and Sequence Generation.

Jason Lee, Dustin Tran, Orhan Firat, Kyunghyun Cho

Proceedings of the Fourth Workshop on Structured Prediction for NLP@EMNLP 2020, 2020

2019

Noise Contrastive Priors for Functional Uncertainty.

Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Discrete Flows: Invertible Generative Models of Discrete Data.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian Layers: A Module for Neural Network Uncertainty.

Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Measuring Calibration in Deep Learning.

Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

2018

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

Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran

CoRR, 2018

Simple, Distributed, and Accelerated Probabilistic Programming.

Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew J. Johnson, Rif A. Saurous

CoRR, 2018

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors.

Danijar Hafner, Dustin Tran, Alex Irpan, Timothy P. Lillicrap, James Davidson

CoRR, 2018

Simple, Distributed, and Accelerated Probabilistic Programming.

Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Mesh-TensorFlow: Deep Learning for Supercomputers.

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Image Transformer.

Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran

Proceedings of the 35th International Conference on Machine Learning, 2018

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

Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger B. Grosse

Proceedings of the 6th International Conference on Learning Representations, 2018

Implicit Causal Models for Genome-wide Association Studies.

Dustin Tran, David M. Blei

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Automatic Differentiation Variational Inference.

Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei

J. Mach. Learn. Res., 2017

TensorFlow Distributions.

Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous

CoRR, 2017

Deep and Hierarchical Implicit Models.

Dustin Tran, Rajesh Ranganath, David M. Blei

CoRR, 2017

Hierarchical Implicit Models and Likelihood-Free Variational Inference.

Dustin Tran, Rajesh Ranganath, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Inference via \chi Upper Bound Minimization.

Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Deep Probabilistic Programming.

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

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Variational Gaussian Process.

Dustin Tran, Rajesh Ranganath, David M. Blei

Proceedings of the 4th International Conference on Learning Representations, 2016

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

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

CoRR, 2016

The $χ$-Divergence for Approximate Inference.

Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei

CoRR, 2016

Operator Variational Inference.

Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Hierarchical Variational Models.

Rajesh Ranganath, Dustin Tran, David M. Blei

Proceedings of the 33nd International Conference on Machine Learning, 2016

Spectral M-estimation with Applications to Hidden Markov Models.

Dustin Tran, Minjae Kim, Finale Doshi-Velez

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Towards Stability and Optimality in Stochastic Gradient Descent.

Panos Toulis, Dustin Tran, Edoardo M. Airoldi

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Variational inference with copula augmentation.

Dustin Tran, David M. Blei, Edoardo M. Airoldi

CoRR, 2015

Stability and optimality in stochastic gradient descent.

Panos Toulis, Dustin Tran, Edoardo M. Airoldi

CoRR, 2015

Copula variational inference.

Dustin Tran, David M. Blei, Edoardo M. Airoldi

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015