← Authors

Dustin Tran

31 papers · 2015–2024 · 7 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (5) 🐝 Cross-Pollinator (5) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (11) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (9) πŸ‘‘ Triple Crown πŸ‘₯ Mega-Team (42) πŸ”¬ Deep Specialist (13) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ—ƒοΈ Keyword Collector (115) πŸ’Ž Century Club (31) πŸ”₯ Unstoppable (7)

Conferences

NIPS (14) ICLR (5) ICML (5) JMLR (3) AISTATS (2) EMNLP (1) UAI (1)

Top co-authors

Balaji Lakshminarayanan (7) David Blei (5) Yeming Wen (5) Jasper Snoek (5) Rajesh Ranganath (5) Rodolphe Jenatton (3) Jeremiah Zhe Liu (3) Ghassen Jerfel (3) Andrew Gelman (2) Mario Lucic (2)

Keywords

variational inference (9) uncertainty quantification (5) posterior approximation (5) bayesian neural network (4) bayesian inference (4) probabilistic programming (4) model calibration (3) autoregressive model (3) spectral normalization (2) deep ensemble (2) neural network (2) normalizing flow (2) predictive uncertainty (2) latent variable (2) out-of-distribution detection (2) markov chain monte carlo (2) distributed computing (2) gaussian process (2) uncertainty estimation (2) ensemble learning (1)

Papers

Long-form factuality in large language models NIPS 2024
A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models ICML 2023
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness JMLR 2023
Scaling Vision Transformers to 22 Billion Parameters ICML 2023
Training independent subnetworks for robust prediction ICLR 2021
Revisiting the Calibration of Modern Neural Networks NIPS 2021
Soft Calibration Objectives for Neural Networks NIPS 2021
Combining Ensembles and Data Augmentation Can Harm Your Calibration ICLR 2021
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data JMLR 2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification NIPS 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness NIPS 2020
BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning ICLR 2020
On the Discrepancy between Density Estimation and Sequence Generation EMNLP 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors ICML 2020
Noise Contrastive Priors for Functional Uncertainty UAI 2019
Bayesian Layers: A Module for Neural Network Uncertainty NIPS 2019
Discrete Flows: Invertible Generative Models of Discrete Data NIPS 2019
Implicit Causal Models for Genome-wide Association Studies ICLR 2018
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches ICLR 2018
Simple, Distributed, and Accelerated Probabilistic Programming NIPS 2018
Image Transformer ICML 2018
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language NIPS 2018
Mesh-TensorFlow: Deep Learning for Supercomputers NIPS 2018
Variational Inference via $\chi$ Upper Bound Minimization NIPS 2017
Automatic Differentiation Variational Inference JMLR 2017
Hierarchical Implicit Models and Likelihood-Free Variational Inference NIPS 2017
Spectral M-estimation with Applications to Hidden Markov Models AISTATS 2016
Operator Variational Inference NIPS 2016
Towards Stability and Optimality in Stochastic Gradient Descent AISTATS 2016
Hierarchical Variational Models ICML 2016
Copula variational inference NIPS 2015