Trevor Campbell

Associate Professor

Statistics • UBC

About

I am an associate professor in the Department of Statistics at the University of British Columbia. My research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and Bayesian theory. I was previously a postdoctoral associate advised by Tamara Broderick in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D. candidate under Jonathan How in the Laboratory for Information and Decision Systems (LIDS) at MIT, and before that I was in the Engineering Science program at the University of Toronto.

Teaching

Software

  • Pigeons.jl (development repository here): Bayesian inference via parallel tempering
  • ShorTeX: LaTeX style file for efficient typesetting
  • Rudaux: course management software
  • DictAuth: simple password authentication with JupyterHub
  • fwirl: asynchronous resource management

Students and Postdocs

Miguel Biron Lattes
Miguel Biron Lattes

Ph.D. Student

Cosupervisor:

Alex Bouchard-Côté

Xinglong Li
Xinglong Li

Ph.D. Student

Nikola Surjanovic
Nikola Surjanovic

Ph.D. Student

Cosupervisor:

Alex Bouchard-Côté

David Xu
David Xu

Ph.D. Student

Naitong Chen
Naitong Chen

Ph.D. Student

Gian Carlo Diluvi
Gian Carlo Diluvi

Ph.D. Student

Cosupervisor:

Benjamin Bloem-Reddy

Ivy Liu
Ivy Liu

M.Sc. Student

Yujia Ma
Yujia Ma

Undergraduate

Books

Data Science:<br>A First Introduction<br>(R Edition)
Data Science:
A First Introduction
(R Edition)

Timbers, Campbell, Lee

Data Science:<br>A First Introduction<br>(Python Edition)
Data Science:
A First Introduction
(Python Edition)

Timbers, Campbell, Lee,
Östblom, Heagy

Preprints

MCMC-driven learning
A. Bouchard-Côté, T. Campbell, G. Pleiss, N. Surjanovic
Pigeons.jl: Distributed sampling from intractable distributions
N. Surjanovic, M. Biron-Lattes, P. Tiede, S. Syed, T. Campbell, A. Bouchard-Côté

Publications

Automatic regenerative simulation via non-reversible simulated tempering
M. Biron-Lattes, T. Campbell, A. Bouchard-Côté
Journal of the American Statistical Association (accepted), 2024+
Coreset Markov chain Monte Carlo
N. Chen, T. Campbell
International Conference on Artificial Intelligence and Statistics, 2024
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
M. Biron-Lattes*, N. Surjanovic*, S. Syed, T. Campbell, A. Bouchard-Côté
International Conference on Artificial Intelligence and Statistics, 2024
Mixed variational flows for discrete variables
G.C. Diluvi, B. Bloem-Reddy, T. Campbell
International Conference on Artificial Intelligence and Statistics, 2024
Emerging directions in Bayesian computation
S. Winter, T. Campbell, L. Lin, S. Srivastava, D. Dunson
Statistical Science 39(1), 62-89, 2024
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
Z. Xu, T. Campbell
Advances in Neural Information Processing Systems, 2023
Conditional permutation invariant flows
B. Zwartsenberg, A. Ścibior, M. Niedoba, V. Lioutas, Y. Liu, J. Sefas, S. Dabiri, J. Lavington, T. Campbell, F. Wood
Transactions on Machine Learning Research, 2023
MixFlows: principled variational inference via mixed flows
Z. Xu, N. Chen, and T. Campbell
International Conference on Machine Learning, 2023
Bayesian inference via sparse Hamiltonian flows
N. Chen, Z. Xu, and T. Campbell
Advances in Neural Information Processing Systems, 2022
Parallel tempering with a variational reference
N. Surjanovic, S. Syed, A. Bouchard-Côté, and T. Campbell
Advances in Neural Information Processing Systems, 2022
Fast Bayesian coresets via subsampling and quasi-Newton refinement
C. Naik, J. Rousseau, and T. Campbell
Advances in Neural Information Processing Systems, 2022
The computational asymptotics of Gaussian variational inference and the Laplace approximation
Z. Xu and T. Campbell
Statistics and Computing 32(63), 2022
Local exchangeability
T. Campbell, S. Syed, C. Yang, M. Jordan, and T. Broderick
Bernoulli 29(3), 2084-2100, 2023
Pseudo-marginal inference for CTMCs on infinite spaces via monotonic likelihood approximations
M. Biron-Lattes, A. Bouchard-Côté, and T. Campbell
Journal of Computational and Graphical Statistics 32(2): 513-527, 2023
The Chicago Police Department dataset
T. Horel, L. Masoero, R. Agrawal, D. Roithmayr, and T. Campbell
Neural Information Processing Systems Track on Datasets and Benchmarks, 2021
Truncated simulation and inference in edge-exchangeable networks
X. Li and T. Campbell
Electronic Journal of Statistics 15(2), 5117-5157, 2021
Parallel tempering on optimized paths
S. Syed*, V. Romaniello*, T. Campbell, and A. Bouchard-Côté
International Conference on Machine Learning, 2021
Finite mixture models do not reliably learn the number of components
D. Cai*, T. Campbell*, and T. Broderick
International Conference on Machine Learning, 2021
Sequential core-set Monte Carlo
B. Beronov, C. Weilbach, F. Wood, and T. Campbell
Uncertainty in Artificial Intelligence, 2021
Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
S. Amini Niaki, E. Haghighat, T. Campbell, A. Poursartip, and R. Vaziri
Computer Methods in Applied Mechanics and Engineering 384, 2021
Bayesian pseudocoresets
D. Manousakas, Z. Xu, C. Mascolo, and T. Campbell
Advances in Neural Information Processing Systems, 2020
Slice sampling for general completely random measures
P. Zhu, A. Bouchard-Côté, and T. Campbell
Uncertainty in Artificial Intelligence, 2020
Validated variational inference via practical posterior error bounds
J. Huggins, M. Kasprzak, T. Campbell, and T. Broderick
International Conference on Artificial Intelligence and Statistics, 2020
Sparse variational inference: Bayesian coresets from scratch
T. Campbell and B. Beronov
Advances in Neural Information Processing Systems, 2019
Universal boosting variational inference
T. Campbell and X. Li
Advances in Neural Information Processing Systems, 2019
Automated scalable Bayesian inference via Hilbert coresets
T. Campbell and T. Broderick
Journal of Machine Learning Research 20(15):1-38, 2019
Data-dependent compression of random features for large-scale kernel approximation
R. Agrawal, T. Campbell, J. Huggins, and T. Broderick
International Conference on Artificial Intelligence and Statistics, 2019
Scalable Gaussian process inference with finite-data mean and variance guarantees
J. Huggins, T. Campbell, M. Kasprzak, and T. Broderick
International Conference on Artificial Intelligence and Statistics, 2019
Bayesian coreset construction via greedy iterative geodesic ascent
T. Campbell and T. Broderick
International Conference on Machine Learning, 2018
Truncated random measures
T. Campbell*, J. Huggins*, J. P. How, and T. Broderick
Bernoulli 25(2), 1256-1288, 2019
Dynamic clustering algorithms via small-variance analysis of Markov chain mixture models
T. Campbell, B. Kulis, and J. P. How
IEEE Transactions on Pattern Analysis and Machine Intelligence 41(6), 1338-1352, 2019
Exchangeable trait allocations
T. Campbell, D. Cai, and T. Broderick
Electronic Journal of Statistics 12(2), 2290-2322, 2018
Efficient global point cloud alignment using Bayesian nonparametric mixtures
J. Straub, T. Campbell, J. P. How, and J. W. Fisher III
IEEE Conference on Computer Vision and Pattern Recognition, 2017
Coresets for scalable Bayesian logistic regression
J. Huggins, T. Campbell, and T. Broderick
Advances in Neural Information Processing Systems, 2016
Edge-exchangeable graphs and sparsity
D. Cai, T. Campbell, and T. Broderick
Advances in Neural Information Processing Systems, 2016
Streaming, distributed variational inference for Bayesian nonparametrics
T. Campbell, J. Straub, J. W. Fisher III, and J. P. How
Advances in Neural Information Processing Systems, 2015
Small-variance nonparametric clustering on the hypersphere
J. Straub, T. Campbell, J. P. How, and J. W. Fisher III
IEEE Conference on Computer Vision and Pattern Recognition, 2015
Bayesian nonparametric set construction for robust optimization
T. Campbell and J. P. How
American Control Conference, 2015
Approximate decentralized Bayesian inference
T. Campbell and J. P. How
Uncertainty in Artificial Intelligence, 2014
Dynamic clustering via asymptotics of the dependent Dirichlet process mixture
T. Campbell, M. Liu, B. Kulis, J. P. How, and L. Carin
Advances in Neural Information Processing Systems, 2013
Multiagent allocation of Markov decision process tasks
T. Campbell, L. Johnson, and J. P. How
American Control Conference, 2013
Simultaneous clustering on representation expansion for learning multimodel MDPs
T. Campbell, R. H. Klein, A. Geramifard, and J. P. How
Reinforcement Learning and Decision Making, 2013
Truncated Bayesian nonparametrics
Ph.D. thesis
Massachusetts Institute of Technology, 2016
Multiagent planning with Bayesian nonparametric asymptotics
Master's thesis
Massachusetts Institute of Technology, 2013

Past Students and Postdocs

Vittorio Romaniello
Vittorio Romaniello

Machine Learning Scientist

Expedia

Kamyar Gordnian
Kamyar Gordnian

Senior Engineer

Convergent Manufacturing Technologies

Sina Amini Niaki
Sina Amini Niaki

Data Scientist

Amazon

Hayden McTavish
Hayden McTavish

Ph.D. Student

UC San Diego

Berend Zwartsenberg
Berend Zwartsenberg

Chief Scientist

Inverted AI

Gary Zhu
Gary Zhu

Statistician

Centre for Heart Lung Innovation