Trevor Campbell

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 52

Top Topics

Variational Inference

Top Venues

Publications

Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell
Mixed variational flows for discrete variables. AISTATS (2024)
Naitong Chen, Trevor Campbell
Coreset Markov chain Monte Carlo. AISTATS (2024)
Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm. AISTATS (2024)
Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic
MCMC-driven learning. CoRR (2024)
Trevor Campbell
General bounds on the quality of Bayesian coresets. CoRR (2024)
Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell
Pseudo-Marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations. J. Comput. Graph. Stat. 32 (2) (2023)
Zuheng Xu, Trevor Campbell
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. NeurIPS (2023)
Zuheng Xu, Naitong Chen, Trevor Campbell
MixFlows: principled variational inference via mixed flows. ICML (2023)
Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson
Machine Learning and the Future of Bayesian Computation. CoRR (2023)
Zuheng Xu, Trevor Campbell
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. CoRR (2023)
Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Justice Sefas, Yunpeng Liu, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood
Conditional Permutation Invariant Flows. Trans. Mach. Learn. Res. 2023 (2023)
Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell
Mixed Variational Flows for Discrete Variables. CoRR (2023)
Nikola Surjanovic, Miguel Biron-Lattes, Paul Tiede, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté
Pigeons.jl: Distributed Sampling From Intractable Distributions. CoRR (2023)
Cian Naik, Judith Rousseau, Trevor Campbell
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. CoRR (2022)
Naitong Chen, Zuheng Xu, Trevor Campbell
Bayesian inference via sparse Hamiltonian flows. CoRR (2022)
Berend Zwartsenberg, Adam Scibior, Matthew Niedoba, Vasileios Lioutas, Yunpeng Liu, Justice Sefas, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood
Conditional Permutation Invariant Flows. CoRR (2022)
Zuheng Xu, Naitong Chen, Trevor Campbell
Ergodic variational flows. CoRR (2022)
Naitong Chen, Zuheng Xu, Trevor Campbell
Bayesian inference via sparse Hamiltonian flows. NeurIPS (2022)
Zuheng Xu, Trevor Campbell
The computational asymptotics of Gaussian variational inference and the Laplace approximation. Stat. Comput. 32 (4) (2022)
Cian Naik, Judith Rousseau, Trevor Campbell
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement. NeurIPS (2022)
Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell
Parallel Tempering With a Variational Reference. NeurIPS (2022)
Thibaut Horel, Lorenzo Masoero, Raj Agrawal, Daria Roithmayr, Trevor Campbell
The CPD Data Set: Personnel, Use of Force, and Complaints in the Chicago Police Department. NeurIPS Datasets and Benchmarks (2021)
Diana Cai, Trevor Campbell, Tamara Broderick
Finite mixture models do not reliably learn the number of components. ICML (2021)
Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell
Sequential core-set Monte Carlo. UAI (2021)
Saifuddin Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Côté
Parallel tempering on optimized paths. ICML (2021)
Sina Amini Niaki, Ehsan Haghighat, Xinglong Li, Trevor Campbell, Reza Vaziri
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture. CoRR (2020)
Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
Slice Sampling for General Completely Random Measures. UAI (2020)
Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
Slice Sampling for General Completely Random Measures. CoRR (2020)
Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell
Bayesian Pseudocoresets. NeurIPS (2020)
Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick
Validated Variational Inference via Practical Posterior Error Bounds. AISTATS (2020)
Trevor Campbell, Boyan Beronov
Sparse Variational Inference: Bayesian Coresets from Scratch. CoRR (2019)
Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick
Practical Posterior Error Bounds from Variational Objectives. CoRR (2019)
Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS (2019)
Trevor Campbell, Xinglong Li
Universal Boosting Variational Inference. CoRR (2019)
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
Data-dependent compression of random features for large-scale kernel approximation. AISTATS (2019)
Trevor Campbell, Tamara Broderick
Automated Scalable Bayesian Inference via Hilbert Coresets. J. Mach. Learn. Res. 20 (2019)
Trevor Campbell, Xinglong Li
Universal Boosting Variational Inference. NeurIPS (2019)
Trevor Campbell, Boyan Beronov
Sparse Variational Inference: Bayesian Coresets from Scratch. NeurIPS (2019)
Trevor Campbell, Brian Kulis, Jonathan P. How
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 41 (6) (2019)
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
Data-dependent compression of random features for large-scale kernel approximation. CoRR (2018)
Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. CoRR (2018)
Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan H. Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. CoRR (2018)
Trevor Campbell, Tamara Broderick
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. CoRR (2018)
Trevor Campbell, Tamara Broderick
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent. ICML (2018)
Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. CoRR (2018)
Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures. CVPR (2017)
Trevor Campbell, Tamara Broderick
Automated Scalable Bayesian Inference via Hilbert Coresets. CoRR (2017)
Diana Cai, Trevor Campbell, Tamara Broderick
Edge-exchangeable graphs and sparsity. NIPS (2016)
Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
Small-Variance Nonparametric Clustering on the Hypersphere. CoRR (2016)
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
Coresets for Scalable Bayesian Logistic Regression. NIPS (2016)
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
Coresets for Scalable Bayesian Logistic Regression. CoRR (2016)
Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
Efficient Globally Optimal Point Cloud Alignment using Bayesian Nonparametric Mixtures. CoRR (2016)
Trevor Campbell, Jonathan P. How
Bayesian nonparametric set construction for robust optimization. ACC (2015)
Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
Streaming, Distributed Variational Inference for Bayesian Nonparametrics. NIPS (2015)
Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
Streaming, Distributed Variational Inference for Bayesian Nonparametrics. CoRR (2015)
Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
Small-variance nonparametric clustering on the hypersphere. CVPR (2015)
Trevor Campbell, Jonathan P. How
Approximate Decentralized Bayesian Inference. UAI (2014)
Trevor Campbell, Jonathan P. How
Decentralized Variational Bayesian Inference. CoRR (2014)
Trevor Campbell, Luke B. Johnson, Jonathan P. How
Multiagent allocation of Markov decision process tasks. ACC (2013)
Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture. NIPS (2013)
Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture CoRR (2013)