Campbell, Trevor

36 publications

NeurIPS 2025 Asymptotically Exact Variational Flows via Involutive MCMC Kernels Zuheng Xu, Trevor Campbell

ICML 2025 AutoStep: Locally Adaptive Involutive MCMC Tiange Liu, Nikola Surjanovic, Miguel Biron-Lattes, Alexandre Bouchard-Cote, Trevor Campbell

AISTATS 2025 Is Gibbs Sampling Faster than Hamiltonian Monte Carlo on GLMs? Son Luu, Zuheng Xu, Nikola Surjanovic, Miguel Biron-Lattes, Trevor Campbell, Alexandre Bouchard-Cote

NeurIPS 2025 Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference Kyurae Kim, Yian Ma, Trevor Campbell, Jacob R. Gardner

ICML 2025 Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell

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

AISTATS 2024 Coreset Markov Chain Monte Carlo Naitong Chen, Trevor Campbell

NeurIPS 2024 General Bounds on the Quality of Bayesian Coresets Trevor Campbell

AISTATS 2024 Mixed Variational Flows for Discrete Variables Gian C. Diluvi, Benjamin Bloem-Reddy, Trevor Campbell

AISTATS 2024 autoMALA: Locally Adaptive Metropolis-Adjusted Langevin Algorithm Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Cote

TMLR 2023 Conditional Permutation Invariant Flows Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Justice Sefas, Yunpeng Liu, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood

NeurIPS 2023 Embracing the Chaos: Analysis and Diagnosis of Numerical Instability in Variational Flows Zuheng Xu, Trevor Campbell

ICML 2023 MixFlows: Principled Variational Inference via Mixed Flows Zuheng Xu, Naitong Chen, Trevor Campbell

NeurIPS 2022 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

ICML 2021 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-Cote

UAI 2021 Sequential Core-Set Monte Carlo Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell

NeurIPS 2020 Bayesian Pseudocoresets Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell

NeurIPSW 2020 Power Posteriors Do Not Reliably Learn the Number of Components in a Finite Mixture Diana Cai, Trevor Campbell, Tamara Broderick

UAI 2020 Slice Sampling for General Completely Random Measures Peiyuan Zhu, Alexandre Bouchard-Cote, Trevor Campbell

AISTATS 2020 Validated Variational Inference via Practical Posterior Error Bounds Jonathan Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

JMLR 2019 Automated Scalable Bayesian Inference via Hilbert Coresets Trevor Campbell, Tamara Broderick

AISTATS 2019 Data-Dependent Compression of Random Features for Large-Scale Kernel Approximation Raj Agrawal, Trevor Campbell, Jonathan 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

NeurIPS 2019 Sparse Variational Inference: Bayesian Coresets from Scratch Trevor Campbell, Boyan Beronov

NeurIPS 2019 Universal Boosting Variational Inference Trevor Campbell, Xinglong Li

ICML 2018 Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent Trevor Campbell, Tamara Broderick

CVPR 2017 Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher Iii

NeurIPS 2016 Coresets for Scalable Bayesian Logistic Regression Jonathan Huggins, Trevor Campbell, Tamara Broderick

NeurIPS 2016 Edge-Exchangeable Graphs and Sparsity Diana Cai, Trevor Campbell, Tamara Broderick

CVPR 2015 Small-Variance Nonparametric Clustering on the Hypersphere Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher Iii

NeurIPS 2015 Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell, Julian Straub, John W. Fisher Iii, Jonathan P How

UAI 2014 Approximate Decentralized Bayesian Inference Trevor Campbell, Jonathan P. How

NeurIPS 2013 Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P How, Lawrence Carin