Trevor Campbell

Assistant Professor

Statistics UBC

About

I am an assistant professor 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.

News

Teaching

Preprints

Practical bounds on the error of Bayesian posterior approximations: a nonasymptotic approach
J. Huggins, M. Kasprzak, T. Campbell and T. Broderick
Data-dependent compression of random features for large-scale kernel approximation
R. Agrawal, T. Campbell, J. Huggins, and T. Broderick
Scalable Gaussian process inference with finite-data mean and variance guarantees
J. Huggins, T. Campbell, M. Kasprzak, and T. Broderick
Automated scalable Bayesian inference via Hilbert coresets
T. Campbell and T. Broderick
Truncated random measures
T. Campbell, J. Huggins, J. P. How, and T. Broderick
Bernoulli (accepted)
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 (accepted)

Publications

Bayesian coreset construction via greedy iterative geodesic ascent
T. Campbell and T. Broderick
International Conference on Machine Learning, 2018
Exchangeable trait allocations
T. Campbell, D. Cai, and T. Broderick
Electronic Journal of Statistics 12(2), 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