Dustin Tran

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

Achievements

+12 more ↓

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (11) 🌍 Conference Polyglot (7)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏃 Academic Marathon (9) 👑 Triple Crown 👥 Mega-Team (42) 🔬 Deep Specialist (13) 📈 Trend Setter 🚀 Conference Pioneer ⚡ Prolific Year (6) 🗃️ Keyword Collector (115) 💎 Century Club (31) 🔥 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