Tran, Dustin

40 publications

NeurIPS 2024 Long-Form Factuality in Large Language Models Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le

JMLR 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

ICML 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

ICML 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

TMLR 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

ICMLW 2022 Plex: Towards Reliability Using Pretrained Large Model Extensions Dustin Tran, Jeremiah Zhe Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda E Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, E. Kelly Buchanan, Kevin Patrick Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

NeurIPSW 2022 Reliability Benchmarks for Image Segmentation E. Kelly Buchanan, Michael W Dusenberry, Jie Ren, Kevin Patrick Murphy, Balaji Lakshminarayanan, Dustin Tran

TMLR 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

NeurIPSW 2021 Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal

ICLR 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

NeurIPS 2021 Revisiting the Calibration of Modern Neural Networks Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic

NeurIPS 2021 Soft Calibration Objectives for Neural Networks Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael Mozer, Becca Roelofs

ICLR 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

ICLR 2020 BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning Yeming Wen, Dustin Tran, Jimmy Ba

ICML 2020 Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yian Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran

JMLR 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

NeurIPS 2020 Hyperparameter Ensembles for Robustness and Uncertainty Quantification Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

NeurIPS 2020 Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, Balaji Lakshminarayanan

NeurIPSW 2020 Why Are Bootstrapped Deep Ensembles Not Better? Jeremy Nixon, Balaji Lakshminarayanan, Dustin Tran

NeurIPS 2019 Bayesian Layers: A Module for Neural Network Uncertainty Dustin Tran, Mike Dusenberry, Mark van der Wilk, Danijar Hafner

NeurIPS 2019 Discrete Flows: Invertible Generative Models of Discrete Data Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole

ICLRW 2019 Discrete Flows: Invertible Generative Models of Discrete Data Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, Ben Poole

CVPRW 2019 Measuring Calibration in Deep Learning Jeremy Nixon, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, Dustin Tran

UAI 2019 Noise Contrastive Priors for Functional Uncertainty Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson

NeurIPS 2018 Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language Matthew D. Hoffman, Matthew J Johnson, Dustin Tran

ICLR 2018 Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse

ICML 2018 Image Transformer Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran

ICLR 2018 Implicit Causal Models for Genome-Wide Association Studies Dustin Tran, David M. Blei

NeurIPS 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 Hechtman

NeurIPS 2018 Simple, Distributed, and Accelerated Probabilistic Programming Dustin Tran, Matthew W Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul

JMLR 2017 Automatic Differentiation Variational Inference Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei

ICLR 2017 Deep Probabilistic Programming Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

NeurIPS 2017 Hierarchical Implicit Models and Likelihood-Free Variational Inference Dustin Tran, Rajesh Ranganath, David Blei

NeurIPS 2017 Variational Inference via $\chi$ Upper Bound Minimization Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Blei

ICML 2016 Hierarchical Variational Models Rajesh Ranganath, Dustin Tran, David Blei

NeurIPS 2016 Operator Variational Inference Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David Blei

AISTATS 2016 Spectral M-Estimation with Applications to Hidden Markov Models Dustin Tran, Minjae Kim, Finale Doshi-Velez

AISTATS 2016 Towards Stability and Optimality in Stochastic Gradient Descent Panos Toulis, Dustin Tran, Edoardo M. Airoldi

ICLR 2016 Variational Gaussian Process Dustin Tran, Rajesh Ranganath, David M. Blei

NeurIPS 2015 Copula Variational Inference Dustin Tran, David Blei, Edoardo M. Airoldi