Trevor Campbell
Trevor Campbell
Assistant Professor, Statistics, UBC
Verified email at stat.ubc.ca - Homepage
Title
Cited by
Cited by
Year
Coresets for scalable Bayesian logistic regression
J Huggins, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4080-4088, 2016
992016
Edge-exchangeable graphs and sparsity
D Cai, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4249-4257, 2016
552016
Dynamic clustering via asymptotics of the dependent Dirichlet process mixture
T Campbell, M Liu, B Kulis, JP How, L Carin
Advances in Neural Information Processing Systems, 449-457, 2013
492013
Automated scalable Bayesian inference via Hilbert coresets
T Campbell, T Broderick
The Journal of Machine Learning Research 20 (1), 551-588, 2019
422019
Bayesian coreset construction via greedy iterative geodesic ascent
T Campbell, T Broderick
arXiv preprint arXiv:1802.01737, 2018
392018
Bayesian nonparametric set construction for robust optimization
T Campbell, JP How
2015 American Control Conference (ACC), 4216-4221, 2015
352015
Streaming, distributed variational inference for Bayesian nonparametrics
T Campbell, J Straub, JW Fisher III, JP How
Advances in Neural Information Processing Systems, 280-288, 2015
302015
Efficient global point cloud alignment using Bayesian nonparametric mixtures
J Straub, T Campbell, JP How, JW Fisher
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
292017
Small-variance nonparametric clustering on the hypersphere
J Straub, T Campbell, JP How, JW Fisher
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015
292015
Truncated random measures
T Campbell, JH Huggins, JP How, T Broderick
Bernoulli 25 (2), 1256-1288, 2019
162019
Exchangeable trait allocations
T Campbell, D Cai, T Broderick
Electronic Journal of Statistics 12 (2), 2290-2322, 2018
142018
Approximate decentralized Bayesian inference
T Campbell, JP How
arXiv preprint arXiv:1403.7471, 2014
142014
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
JH Huggins, T Campbell, M Kasprzak, T Broderick
arXiv preprint arXiv:1809.09505, 2018
132018
Multiagent allocation of markov decision process tasks
T Campbell, L Johnson, JP How
2013 American Control Conference, 2356-2361, 2013
112013
Scalable Gaussian process inference with finite-data mean and variance guarantees
JH Huggins, T Campbell, M Kasprzak, T Broderick
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
102019
Data-dependent compression of random features for large-scale kernel approximation
R Agrawal, T Campbell, J Huggins, T Broderick
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
82019
Universal boosting variational inference
T Campbell, X Li
Advances in Neural Information Processing Systems, 3484-3495, 2019
82019
Practical posterior error bounds from variational objectives
J Huggins, M Kasprzak, T Campbell, B Tamara
Proceedings of the 23rd International Conference on Artificial Intelligence …, 2020
52020
Validated variational inference via practical posterior error bounds
J Huggins, M Kasprzak, T Campbell, T Broderick
International Conference on Artificial Intelligence and Statistics, 1792-1802, 2020
42020
Sparse variational inference: Bayesian coresets from scratch
T Campbell, B Beronov
Advances in Neural Information Processing Systems, 11461-11472, 2019
42019
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Articles 1–20