Thang D Bui
Title
Cited by
Cited by
Year
Variational continual learning
CV Nguyen, Y Li, TD Bui, RE Turner
International Conference on Learning Representations (ICLR), 2018
3802018
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
1852016
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
1732016
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
113*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
90*2018
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
602015
Streaming sparse Gaussian process approximations
TD Bui, CV Nguyen, RE Turner
Advances in Neural Information Processing Systems 30 (NeurIPS), 2017
572017
Tree-structured Gaussian Process Approximations
TD Bui, RE Turner
Advances in Neural Information Processing Systems, 2213-2221, 2014
462014
Improving and understanding variational continual learning
S Swaroop, CV Nguyen, TD Bui, RE Turner
arXiv preprint arXiv:1905.02099, 2019
272019
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
192018
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
172015
Design of covariance functions using inter-domain inducing variables
F Tobar, TD Bui, RE Turner
NIPS Time Series Workshop, 2015
122015
Stochastic variational inference for Gaussian process latent variable models using back constraints
TD Bui, RE Turner
Black Box Learning and Inference NIPS workshop, 2015
122015
Hierarchical Gaussian process priors for Bayesian neural network weights
T Karaletsos, TD Bui
Advances in Neural Information Processing Systems 33 (NeurIPS), 2020
9*2020
Natural Variational Continual Learning
H Tseran, ME Khan, T Harada, TD Bui
NeurIPS Continual Learning Workshop, 2018
82018
Online Variational Bayesian Inference: Algorithms for Sparse Gaussian Processes and Theoretical Bounds
CV Nguyen, TD Bui, Y Li, RE Turner
ICML Time Series Workshop, 2017
72017
Annealed importance sampling with q-paths
R Brekelmans, V Masrani, T Bui, F Wood, A Galstyan, GV Steeg, ...
arXiv preprint arXiv:2012.07823, 2020
52020
Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
TD Bui
University of Cambridge, 2017
52017
Variational auto-regressive gaussian processes for continual learning
S Kapoor, T Karaletsos, TD Bui
International Conference on Machine Learning, 5290-5300, 2021
12021
Bayesian Gaussian process state-space models via Power-EP
T Bui, RE Turner, CE Rasmussen
ICML 2016 Workshop on Data efficient Machine Learning, 2016
12016
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Articles 1–20