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Dustin Tran
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2020 – today
- 2024
- [i41]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 abs/2403.18802 (2024) - 2023
- [j6]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: 42:1-42:63 (2023) - [c33]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: 547-568 - [c32]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: 7480-7512 - [i40]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 Nader 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 abs/2302.05442 (2023) - [i39]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 abs/2302.06235 (2023) - [i38]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 abs/2303.03846 (2023) - 2022
- [j5]James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (2022) - [j4]Vincent Fortuin
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022) - [i37]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 abs/2205.00403 (2022) - [i36]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:
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR abs/2207.07411 (2022) - [i35]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 abs/2211.12717 (2022) - 2021
- [j3]Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato:
Sampling the Variational Posterior with Local Refinement. Entropy 23(11): 1475 (2021) - [c31]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 - [c30]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 - [c29]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 - [c28]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: 15682-15694 - [c27]Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs:
Soft Calibration Objectives for Neural Networks. NeurIPS 2021: 29768-29779 - [i34]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 abs/2103.08057 (2021) - [i33]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
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i32]Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic:
Revisiting the Calibration of Modern Neural Networks. CoRR abs/2106.07998 (2021) - [i31]Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C. Mozer, Becca Roelofs:
Soft Calibration Objectives for Neural Networks. CoRR abs/2108.00106 (2021) - [i30]Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou:
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR abs/2110.02609 (2021) - [i29]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 abs/2110.03360 (2021) - 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
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 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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