Publications (55)
Revisiting the Calibration of Modern Neural Networks
Matthias Minderer, Josip Djolonga, Rob Romijnders +5
Discussion of "Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing"
Dustin Tran, David M. Blei
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team, Rohan Anil, Sebastian Borgeaud +1340
Analyzing the Role of Model Uncertainty for Electronic Health Records
Michael W. Dusenberry, Dustin Tran, Edward Choi +5
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren +7
Stochastic gradient descent methods for estimation with large data sets
Dustin Tran, Panos Toulis, Edoardo M. Airoldi
Implicit Causal Models for Genome-wide Association Studies
Dustin Tran, David M. Blei
Deep Classifiers with Label Noise Modeling and Distance Awareness
Vincent Fortuin, Mark Collier, Florian Wenzel +7
Gemma 3 Technical Report
Gemma Team, Aishwarya Kamath, Johan Ferret +209
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado, Neil Band, Mark Collier +23
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen +5
BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning
Yeming Wen, Dustin Tran, Jimmy Ba
Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore +5
Operator Variational Inference
Rajesh Ranganath, Jaan Altosaar, Dustin Tran +1
Non-standard Symplectic Structures via Symplectic Cohomology
Dustin Tran
The Variational Gaussian Process
Dustin Tran, Rajesh Ranganath, David M. Blei
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
Aki Vehtari, Andrew Gelman, Tuomas Sivula +7
Bayesian Layers: A Module for Neural Network Uncertainty
Dustin Tran, Michael W. Dusenberry, Mark van der Wilk +1
Automatic Differentiation Variational Inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath +2
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy +3
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models
James Urquhart Allingham, Jie Ren, Michael W Dusenberry +5
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Dustin Tran, Rajesh Ranganath, David M. Blei
On the Discrepancy between Density Estimation and Sequence Generation
Jason Lee, Dustin Tran, Orhan Firat +1
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
Copula variational inference
Dustin Tran, David M. Blei, Edoardo M. Airoldi
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon +3
Edward: A library for probabilistic modeling, inference, and criticism
Dustin Tran, Alp Kucukelbir, Adji B. Dieng +3
Convex Techniques for Model Selection
Dustin Tran
Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort +5
Combining Ensembles and Data Augmentation can Harm your Calibration
Yeming Wen, Ghassen Jerfel, Rafael Muller +4
Mesh-TensorFlow: Deep Learning for Supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar +9
Plex: Towards Reliability using Pretrained Large Model Extensions
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry +23
Model Criticism for Bayesian Causal Inference
Dustin Tran, Francisco J. R. Ruiz, Susan Athey +1
Spectral M-estimation with Applications to Hidden Markov Models
Dustin Tran, Minjae Kim, Finale Doshi-Velez
Measuring Calibration in Deep Learning
Jeremy Nixon, Mike Dusenberry, Ghassen Jerfel +4
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
Noise Contrastive Priors for Functional Uncertainty
Danijar Hafner, Dustin Tran, Timothy Lillicrap +2
Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, David M. Blei
Towards stability and optimality in stochastic gradient descent
Panos Toulis, Dustin Tran, Edoardo M. Airoldi
On the Theory of Stein Manifolds
Dustin Tran
TensorFlow Distributions
Joshua V. Dillon, Ian Langmore, Dustin Tran +7
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39
Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay +8
Variational Inference via -Upper Bound Minimization
Adji B. Dieng, Dustin Tran, Rajesh Ranganath +2
Image Transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit +4
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Yeming Wen, Paul Vicol, Jimmy Ba +2
Soft Calibration Objectives for Neural Networks
Archit Karandikar, Nicholas Cain, Dustin Tran +4
Sparse MoEs meet Efficient Ensembles
James Urquhart Allingham, Florian Wenzel, Zelda E Mariet +10
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran
Long-form factuality in large language models
Jerry Wei, Chengrun Yang, Xinying Song +9
Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal +2
Deep Probabilistic Programming
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous +3
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
Neil Band, Tim G. J. Rudner, Qixuan Feng +6
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Martin Mladenov, Chih-Wei Hsu, Vihan Jain +7
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Florian Wenzel, Jasper Snoek, Dustin Tran +1