Trevor Campbell

According to our database, Trevor Campbell authored at least 48 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Tuning-free coreset Markov chain Monte Carlo.

Naitong Chen, Jonathan H. Huggins, Trevor Campbell

CoRR, 2024

AutoStep: Locally adaptive involutive MCMC.

Tiange Liu, Nikola Surjanovic, Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell

CoRR, 2024

General bounds on the quality of Bayesian coresets.

Trevor Campbell

CoRR, 2024

MCMC-driven learning.

Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic

CoRR, 2024

Mixed variational flows for discrete variables.

Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Coreset Markov chain Monte Carlo.

Naitong Chen, Trevor Campbell

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm.

Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

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., April, 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

Trans. Mach. Learn. Res., 2023

Pigeons.jl: Distributed Sampling From Intractable Distributions.

Nikola Surjanovic, Miguel Biron-Lattes, Paul Tiede, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté

CoRR, 2023

Machine Learning and the Future of Bayesian Computation.

Steven Winter, Trevor Campbell, Lizhen Lin, Sanvesh Srivastava, David B. Dunson

CoRR, 2023

Embracing the chaos: analysis and diagnosis of numerical instability in variational flows.

Zuheng Xu, Trevor Campbell

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

MixFlows: principled variational inference via mixed flows.

Zuheng Xu, Naitong Chen, Trevor Campbell

Proceedings of the International Conference on Machine Learning, 2023

2022

The computational asymptotics of Gaussian variational inference and the Laplace approximation.

Zuheng Xu, Trevor Campbell

Stat. Comput., 2022

Ergodic variational flows.

Zuheng Xu, Naitong Chen, Trevor Campbell

CoRR, 2022

Parallel Tempering With a Variational Reference.

Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement.

Cian Naik, Judith Rousseau, Trevor Campbell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bayesian inference via sparse Hamiltonian flows.

Naitong Chen, Zuheng Xu, Trevor Campbell

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Sequential core-set Monte Carlo.

Boyan Beronov, Christian Weilbach, Frank Wood, Trevor Campbell

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Parallel tempering on optimized paths.

Saifuddin Syed, Vittorio Romaniello, Trevor Campbell, Alexandre Bouchard-Côté

Proceedings of the 38th International Conference on Machine Learning, 2021

Finite mixture models do not reliably learn the number of components.

Diana Cai, Trevor Campbell, Tamara Broderick

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Slice Sampling for General Completely Random Measures.

Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Bayesian Pseudocoresets.

Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Validated Variational Inference via Practical Posterior Error Bounds.

Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Automated Scalable Bayesian Inference via Hilbert Coresets.

Trevor Campbell, Tamara Broderick

J. Mach. Learn. Res., 2019

Practical Posterior Error Bounds from Variational Objectives.

Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

CoRR, 2019

Universal Boosting Variational Inference.

Trevor Campbell, Xinglong Li

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Sparse Variational Inference: Bayesian Coresets from Scratch.

Trevor Campbell, Boyan Beronov

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees.

Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Data-dependent compression of random features for large-scale kernel approximation.

Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

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

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach.

Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick

CoRR, 2018

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent.

Trevor Campbell, Tamara Broderick

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures.

Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

Efficient Globally Optimal Point Cloud Alignment using Bayesian Nonparametric Mixtures.

Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III

CoRR, 2016

Coresets for Scalable Bayesian Logistic Regression.

Jonathan H. Huggins, Trevor Campbell, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Edge-exchangeable graphs and sparsity.

Diana Cai, Trevor Campbell, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Streaming, Distributed Variational Inference for Bayesian Nonparametrics.

Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Small-variance nonparametric clustering on the hypersphere.

Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Bayesian nonparametric set construction for robust optimization.

Trevor Campbell, Jonathan P. How

Proceedings of the American Control Conference, 2015

2014

Decentralized Variational Bayesian Inference.

Trevor Campbell, Jonathan P. How

CoRR, 2014

Approximate Decentralized Bayesian Inference.

Trevor Campbell, Jonathan P. How

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

2013

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How

CoRR, 2013

Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture.

Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Multiagent allocation of Markov decision process tasks.

Trevor Campbell, Luke B. Johnson, Jonathan P. How

Proceedings of the American Control Conference, 2013