Thang D Bui
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Variational continual learning
CV Nguyen, Y Li, TD Bui, RE Turner
International Conference on Learning Representations (ICLR), 2018
Deep Gaussian processes for regression using approximate expectation propagation
TD Bui, D Hernández-Lobato, Y Li, JM Hernández-Lobato, RE Turner
Proceedings of The 33rd International Conference on Machine Learning (ICML), 2016
Black-box α-divergence minimization
JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, T Bui, ...
Proceedings of The 33rd International Conference on Machine Learning (ICML), 2016
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
TD Bui, J Yan, RE Turner
Journal of Machine Learning Research 18 (104), 1-72, 2017
Neural graph learning: Training neural networks using graphs
TD Bui, S Ravi, V Ramavajjala
Proceedings of the Eleventh ACM International Conference on Web Search and …, 2018
Streaming sparse Gaussian process approximations
TD Bui, CV Nguyen, RE Turner
Advances in Neural Information Processing Systems 30 (NeurIPS), 2017
Learning stationary time series using Gaussian processes with nonparametric kernels
F Tobar, T Bui, R Turner
Advances in Neural Information Processing Systems 28 (NeurIPS), 2015
Partitioned variational inference: A unified framework encompassing federated and continual learning
TD Bui, CV Nguyen, S Swaroop, RE Turner
arXiv preprint arXiv:1811.11206, 2018
Tree-structured Gaussian Process Approximations
TD Bui, RE Turner
Advances in Neural Information Processing Systems, 2213-2221, 2014
Improving and understanding variational continual learning
S Swaroop, CV Nguyen, TD Bui, RE Turner
arXiv preprint arXiv:1905.02099, 2019
Hierarchical Gaussian process priors for Bayesian neural network weights
T Karaletsos, TD Bui
Advances in Neural Information Processing Systems 33 (NeurIPS), 2020
Variational auto-regressive Gaussian processes for continual learning
S Kapoor, T Karaletsos, TD Bui
International Conference on Machine Learning, 5290-5300, 2021
Training deep Gaussian processes using stochastic expectation propagation and probabilistic backpropagation
TD Bui, JM Hernández-Lobato, Y Li, D Hernández-Lobato, RE Turner
arXiv preprint arXiv:1511.03405, 2015
Stochastic variational inference for Gaussian process latent variable models using back constraints
TD Bui, RE Turner
Black Box Learning and Inference NIPS workshop, 2015
q-Paths: Generalizing the Geometric Annealing Path using Power Means
V Masrani, R Brekelmans, T Bui, F Nielsen, A Galstyan, GV Steeg, ...
37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Natural Variational Continual Learning
H Tseran, ME Khan, T Harada, TD Bui
NeurIPS Continual Learning Workshop, 2018
Design of covariance functions using inter-domain inducing variables
F Tobar, TD Bui, RE Turner
NIPS Time Series Workshop, 2015
Partitioned variational inference: A framework for probabilistic federated learning
M Ashman, TD Bui, CV Nguyen, S Markou, A Weller, S Swaroop, ...
arXiv preprint arXiv:2202.12275, 2022
Annealed importance sampling with q-paths
R Brekelmans, V Masrani, T Bui, F Wood, A Galstyan, GV Steeg, ...
arXiv preprint arXiv:2012.07823, 2020
Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
TD Bui
University of Cambridge, 2017
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