Trevor Campbell
Trevor Campbell
Assistant Professor, Statistics, UBC
Email verificata su stat.ubc.ca - Home page
TitoloCitata daAnno
Coresets for scalable Bayesian logistic regression
J Huggins, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4080-4088, 2016
722016
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
442013
Edge-exchangeable graphs and sparsity
D Cai, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4249-4257, 2016
422016
Bayesian nonparametric set construction for robust optimization
T Campbell, JP How
2015 American Control Conference (ACC), 4216-4221, 2015
302015
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
282015
Automated scalable Bayesian inference via Hilbert coresets
T Campbell, T Broderick
The Journal of Machine Learning Research 20 (1), 551-588, 2019
262019
Bayesian coreset construction via greedy iterative geodesic ascent
T Campbell, T Broderick
arXiv preprint arXiv:1802.01737, 2018
252018
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
232017
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
212015
Exchangeable trait allocations
T Campbell, D Cai, T Broderick
Electronic Journal of Statistics 12 (2), 2290-2322, 2018
122018
Approximate decentralized Bayesian inference
T Campbell, JP How
arXiv preprint arXiv:1403.7471, 2014
112014
Truncated random measures
T Campbell, JH Huggins, JP How, T Broderick
Bernoulli 25 (2), 1256-1288, 2019
82019
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
72018
Scalable Gaussian process inference with finite-data mean and variance guarantees
JH Huggins, T Campbell, M Kasprzak, T Broderick
arXiv preprint arXiv:1806.10234, 2018
62018
Multiagent allocation of markov decision process tasks
T Campbell, L Johnson, JP How
2013 American Control Conference, 2356-2361, 2013
62013
Data-dependent compression of random features for large-scale kernel approximation
R Agrawal, T Campbell, JH Huggins, T Broderick
arXiv preprint arXiv:1810.04249, 2018
52018
Multiagent planning with bayesian nonparametric asymptotics
TDJ Campbell
Massachusetts Institute of Technology, 2013
32013
Dynamic clustering algorithms via small-variance analysis of Markov chain mixture models
T Campbell, B Kulis, J How
IEEE transactions on pattern analysis and machine intelligence 41 (6), 1338-1352, 2018
22018
Sparse Variational Inference: Bayesian Coresets from Scratch
T Campbell, B Beronov
Advances in Neural Information Processing Systems, 11457-11468, 2019
12019
Universal boosting variational inference
T Campbell, X Li
Advances in Neural Information Processing Systems, 3479-3490, 2019
12019
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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