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Trevor Campbell
Trevor Campbell
Associate Professor, Statistics, UBC
E-mail confirmado em stat.ubc.ca - Página inicial
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Coresets for scalable Bayesian logistic regression
J Huggins, T Campbell, T Broderick
Advances in neural information processing systems 29, 2016
2742016
Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
SA Niaki, E Haghighat, T Campbell, A Poursartip, R Vaziri
Computer Methods in Applied Mechanics and Engineering 384, 113959, 2021
2402021
Bayesian coreset construction via greedy iterative geodesic ascent
T Campbell, T Broderick
International Conference on Machine Learning, 698-706, 2018
1592018
Automated scalable Bayesian inference via Hilbert coresets
T Campbell, T Broderick
Journal of Machine Learning Research 20 (15), 1-38, 2019
1422019
Edge-exchangeable graphs and sparsity
D Cai, T Campbell, T Broderick
Advances in Neural Information Processing Systems 29, 2016
982016
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
68*2020
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 26, 2013
612013
Bayesian nonparametric set construction for robust optimization
T Campbell, JP How
2015 American Control Conference (ACC), 4216-4221, 2015
582015
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
562017
Sparse variational inference: Bayesian coresets from scratch
T Campbell, B Beronov
Advances in Neural Information Processing Systems 32, 2019
502019
Finite mixture models do not reliably learn the number of components
D Cai, T Campbell, T Broderick
International conference on machine learning, 1158-1169, 2021
442021
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
392018
Streaming, distributed variational inference for Bayesian nonparametrics
T Campbell, J Straub, JW Fisher III, JP How
Advances in Neural Information Processing Systems 28, 2015
392015
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
362015
Bayesian pseudocoresets
D Manousakas, Z Xu, C Mascolo, T Campbell
Advances in Neural Information Processing Systems 33, 14950-14960, 2020
352020
Universal boosting variational inference
T Campbell, X Li
Advances in Neural Information Processing Systems 32, 2019
342019
Truncated random measures
T Campbell, JH Huggins, JP How, T Broderick
312019
Parallel tempering on optimized paths
S Syed, V Romaniello, T Campbell, A Bouchard-Côté
International Conference on Machine Learning, 10033-10042, 2021
292021
Exchangeable trait allocations
T Campbell, D Cai, T Broderick
292018
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
252019
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