Multiagent allocation of Markov decision process tasks. - Trevor Campbell; Luke B. Johnson; Jonathan P. How (ACC, 2013)

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. - Trevor Campbell; Miao Liu 001; Brian Kulis; Jonathan P. How; Lawrence Carin (NIPS, 2013)

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture - Trevor Campbell; Miao Liu 001; Brian Kulis; Jonathan P. How (CoRR, 2013)

Approximate Decentralized Bayesian Inference. - Trevor Campbell; Jonathan P. How (UAI, 2014)

Decentralized Variational Bayesian Inference. - Trevor Campbell; Jonathan P. How (CoRR, 2014)

Bayesian nonparametric set construction for robust optimization. - Trevor Campbell; Jonathan P. How (ACC, 2015)

Small-variance nonparametric clustering on the hypersphere. - Julian Straub; Trevor Campbell; Jonathan P. How; John W. Fisher III (CVPR, 2015)

Streaming, Distributed Variational Inference for Bayesian Nonparametrics. - Trevor Campbell; Julian Straub; John W. Fisher III; Jonathan P. How (NIPS, 2015)

Edge-exchangeable graphs and sparsity. - Diana Cai; Trevor Campbell; Tamara Broderick (NIPS, 2016)

Coresets for Scalable Bayesian Logistic Regression. - Jonathan H. Huggins; Trevor Campbell; Tamara Broderick (NIPS, 2016)

Efficient Globally Optimal Point Cloud Alignment using Bayesian Nonparametric Mixtures. - Julian Straub; Trevor Campbell; Jonathan P. How; John W. Fisher III (CoRR, 2016)

Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures. - Julian Straub; Trevor Campbell; Jonathan P. How; John W. Fisher III (CVPR, 2017)

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. - Trevor Campbell; Tamara Broderick (ICML, 2018)

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. - Jonathan H. Huggins; Trevor Campbell; Mikolaj Kasprzak; Tamara Broderick (CoRR, 2018)

Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. - Miriam Shiffman; William T. Stephenson; Geoffrey Schiebinger; Jonathan H. Huggins; Trevor Campbell; Aviv Regev; Tamara Broderick (CoRR, 2018)

Data-dependent compression of random features for large-scale kernel approximation. - Raj Agrawal; Trevor Campbell; Jonathan H. Huggins; Tamara Broderick (AISTATS, 2019)

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. - Jonathan H. Huggins; Trevor Campbell; Mikolaj Kasprzak; Tamara Broderick (AISTATS, 2019)

Sparse Variational Inference: Bayesian Coresets from Scratch. - Trevor Campbell; Boyan Beronov (NeurIPS, 2019)

Universal Boosting Variational Inference. - Trevor Campbell; Xinglong Li (NeurIPS, 2019)

Practical Posterior Error Bounds from Variational Objectives. - Jonathan H. Huggins; Mikolaj Kasprzak; Trevor Campbell; Tamara Broderick (CoRR, 2019)

Automated Scalable Bayesian Inference via Hilbert Coresets. - Trevor Campbell; Tamara Broderick (J. Mach. Learn. Res., 2019)

Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models. - Trevor Campbell; Brian Kulis; Jonathan P. How (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Validated Variational Inference via Practical Posterior Error Bounds. - Jonathan H. Huggins; Mikolaj Kasprzak; Trevor Campbell; Tamara Broderick (AISTATS, 2020)

Bayesian Pseudocoresets. - Dionysis Manousakas; Zuheng Xu; Cecilia Mascolo; Trevor Campbell (NeurIPS, 2020)

Slice Sampling for General Completely Random Measures. - Peiyuan Zhu; Alexandre Bouchard-Côté; Trevor Campbell (UAI, 2020)

Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture. - Sina Amini Niaki; Ehsan Haghighat; Xinglong Li; Trevor Campbell; Reza Vaziri (CoRR, 2020)

Finite mixture models do not reliably learn the number of components. - Diana Cai; Trevor Campbell; Tamara Broderick (ICML, 2021)

Parallel tempering on optimized paths. - Saifuddin Syed; Vittorio Romaniello; Trevor Campbell; Alexandre Bouchard-Côté (ICML, 2021)

The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department. - Thibaut Horel; Lorenzo Masoero; Raj Agrawal; Daria Roithmayr; Trevor Campbell (NeurIPS Datasets and Benchmarks, 2021)

Sequential core-set Monte Carlo. - Boyan Beronov; Christian Weilbach; Frank Wood; Trevor Campbell (UAI, 2021)

Bayesian inference via sparse Hamiltonian flows. - Naitong Chen; Zuheng Xu; Trevor Campbell (NeurIPS, 2022)

Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. - Cian Naik; Judith Rousseau; Trevor Campbell (NeurIPS, 2022)

Parallel Tempering With a Variational Reference. - Nikola Surjanovic; Saifuddin Syed; Alexandre Bouchard-Côté; Trevor Campbell (NeurIPS, 2022)

Ergodic variational flows. - Zuheng Xu; Naitong Chen; Trevor Campbell (CoRR, 2022)

The computational asymptotics of Gaussian variational inference and the Laplace approximation. - Zuheng Xu; Trevor Campbell (Stat. Comput., 2022)

MixFlows: principled variational inference via mixed flows. - Zuheng Xu; Naitong Chen; Trevor Campbell (ICML, 2023)

Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. - Zuheng Xu; Trevor Campbell (NeurIPS, 2023)

Machine Learning and the Future of Bayesian Computation. - Steven Winter; Trevor Campbell; Lizhen Lin; Sanvesh Srivastava; David B. Dunson (CoRR, 2023)

Pigeons.jl: Distributed Sampling From Intractable Distributions. - Nikola Surjanovic; Miguel Biron-Lattes; Paul Tiede; Saifuddin Syed; Trevor Campbell; Alexandre Bouchard-Côté (CoRR, 2023)

Conditional Permutation Invariant Flows. - Berend Zwartsenberg; Adam Scibior; Matthew Niedoba; Vasileios Lioutas; Justice Sefas; Yunpeng Liu 007; Setareh Dabiri; Jonathan Wilder Lavington; Trevor Campbell; Frank Wood (Trans. Mach. Learn. Res., 2023)

Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations. - Miguel Biron-Lattes; Alexandre Bouchard-Côté; Trevor Campbell (J. Comput. Graph. Stat., 2023)

autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm. - Miguel Biron-Lattes; Nikola Surjanovic; Saifuddin Syed; Trevor Campbell; Alexandre Bouchard-Côté (AISTATS, 2024)

Coreset Markov chain Monte Carlo. - Naitong Chen; Trevor Campbell (AISTATS, 2024)

Mixed variational flows for discrete variables. - Gian Carlo Diluvi; Benjamin Bloem-Reddy; Trevor Campbell (AISTATS, 2024)

General bounds on the quality of Bayesian coresets. - Trevor Campbell (NeurIPS, 2024)

MCMC-driven learning. - Alexandre Bouchard-Côté; Trevor Campbell; Geoff Pleiss; Nikola Surjanovic (CoRR, 2024)

AutoStep: Locally adaptive involutive MCMC. - Tiange Liu; Nikola Surjanovic; Miguel Biron-Lattes; Alexandre Bouchard-Côté; Trevor Campbell (CoRR, 2024)

Tuning-free coreset Markov chain Monte Carlo. - Naitong Chen; Jonathan H. Huggins; Trevor Campbell (CoRR, 2024)

Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs? - Son Luu; Zuheng Xu; Nikola Surjanovic; Miguel Biron-Lattes; Trevor Campbell; Alexandre Bouchard-Côté (AISTATS, 2025)

Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG. - Naitong Chen; Jonathan H. Huggins; Trevor Campbell (UAI, 2025)

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. - Kyurae Kim; Zuheng Xu; Jacob R. Gardner; Trevor Campbell (CoRR, 2025)

AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent. - Nikola Surjanovic; Alexandre Bouchard-Côté; Trevor Campbell (CoRR, 2025)

Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference. - Kyurae Kim; Yi-An Ma; Trevor Campbell; Jacob R. Gardner (CoRR, 2025)