Coresets for scalable Bayesian logistic regression J Huggins, T Campbell, T Broderick Advances in neural information processing systems 29, 2016 | 250 | 2016 |
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 | 174 | 2021 |
Bayesian coreset construction via greedy iterative geodesic ascent T Campbell, T Broderick International Conference on Machine Learning, 698-706, 2018 | 143 | 2018 |
Automated scalable Bayesian inference via Hilbert coresets T Campbell, T Broderick Journal of Machine Learning Research 20 (15), 1-38, 2019 | 122 | 2019 |
Edge-exchangeable graphs and sparsity D Cai, T Campbell, T Broderick Advances in Neural Information Processing Systems 29, 2016 | 93 | 2016 |
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 | 63 | 2013 |
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 | 57* | 2020 |
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 | 54 | 2017 |
Bayesian nonparametric set construction for robust optimization T Campbell, JP How 2015 American Control Conference (ACC), 4216-4221, 2015 | 53 | 2015 |
Sparse variational inference: Bayesian coresets from scratch T Campbell, B Beronov Advances in Neural Information Processing Systems 32, 2019 | 41 | 2019 |
Streaming, distributed variational inference for Bayesian nonparametrics T Campbell, J Straub, JW Fisher III, JP How Advances in Neural Information Processing Systems 28, 2015 | 39 | 2015 |
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 | 38 | 2018 |
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 | 36 | 2015 |
Bayesian pseudocoresets D Manousakas, Z Xu, C Mascolo, T Campbell Advances in Neural Information Processing Systems 33, 14950-14960, 2020 | 32 | 2020 |
Universal boosting variational inference T Campbell, X Li Advances in Neural Information Processing Systems 32, 2019 | 30 | 2019 |
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 | 29 | 2021 |
Truncated random measures T Campbell, JH Huggins, JP How, T Broderick | 28 | 2019 |
Exchangeable trait allocations T Campbell, D Cai, T Broderick | 27 | 2018 |
Parallel tempering on optimized paths S Syed, V Romaniello, T Campbell, A Bouchard-Côté International Conference on Machine Learning, 10033-10042, 2021 | 23 | 2021 |
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 | 23 | 2019 |