← Authors

Trevor Campbell

31 papers · 2013–2025 · 6 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (13) 🐝 Cross-Pollinator (11) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (12) πŸ”¬ Deep Specialist (18) πŸ† Keyword Champion (5) πŸ—ƒοΈ Keyword Collector (106) ⚑ Prolific Year (5) πŸ’Ž Century Club (31) πŸ”₯ Unstoppable (11) πŸ“ˆ Trend Setter ❓ The Questioner

Conferences

NIPS (12) AISTATS (7) ICML (6) UAI (3) CVPR (2) JMLR (1)

Top co-authors

Tamara Broderick (8) Zuheng Xu (6) Alexandre Bouchard-cΓ΄tΓ© (6) Naitong Chen (4) Nikola Surjanovic (4) Julian Straub (3) Saifuddin Syed (3) Miguel Biron-Lattes (3) John W. Fisher III (3) Jonathan Huggins (3)

Research topics

Differential Privacy (1)

Keywords

bayesian inference (11) variational inference (9) markov chain monte carlo (8) posterior approximation (8) posterior inference (5) bayesian nonparametrics (4) bayesian coreset (4) uncertainty quantification (3) kl divergence (2) density evaluation (2) parallel tempering (2) normalizing flow (2) bayesian nonparametric (2) variational flow (2) importance sampling (2) stochastic process (2) coreset construction (2) measure-preserving map (2) nonparametric bayesian (1) graph theory (1)

Papers

Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs? AISTATS 2025
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG UAI 2025
AutoStep: Locally adaptive involutive MCMC ICML 2025
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization ICML 2025
Mixed variational flows for discrete variables AISTATS 2024
General bounds on the quality of Bayesian coresets NIPS 2024
Coreset Markov chain Monte Carlo AISTATS 2024
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm AISTATS 2024
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows NIPS 2023
MixFlows: principled variational inference via mixed flows ICML 2023
Parallel Tempering With a Variational Reference NIPS 2022
Bayesian inference via sparse Hamiltonian flows NIPS 2022
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement NIPS 2022
Parallel tempering on optimized paths ICML 2021
Finite mixture models do not reliably learn the number of components ICML 2021
Sequential core-set Monte Carlo UAI 2021
Bayesian Pseudocoresets NIPS 2020
Slice Sampling for General Completely Random Measures UAI 2020
Validated Variational Inference via Practical Posterior Error Bounds AISTATS 2020
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees AISTATS 2019
Universal Boosting Variational Inference NIPS 2019
Automated Scalable Bayesian Inference via Hilbert Coresets JMLR 2019
Sparse Variational Inference: Bayesian Coresets from Scratch NIPS 2019
Data-dependent compression of random features for large-scale kernel approximation AISTATS 2019
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent ICML 2018
Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures CVPR 2017
Coresets for Scalable Bayesian Logistic Regression NIPS 2016
Edge-exchangeable graphs and sparsity NIPS 2016
Small-Variance Nonparametric Clustering on the Hypersphere CVPR 2015
Streaming, Distributed Variational Inference for Bayesian Nonparametrics NIPS 2015
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture NIPS 2013