Thang D Bui
Thang D Bui
Research Scientist, Uber AI; Lecturer, University of Sydney
Verified email at - Homepage
Cited by
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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
Learning stationary time series using Gaussian processes with nonparametric kernels
F Tobar, T Bui, R Turner
Streaming sparse Gaussian process approximations
TD Bui, CV Nguyen, RE Turner
arXiv preprint arXiv:1705.07131, 2017
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
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
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
Design of covariance functions using inter-domain inducing variables
F Tobar, TD Bui, RE Turner
NIPS Time Series Workshop, 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
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
Natural Variational Continual Learning
H Tseran, ME Khan, T Harada, TD Bui
NeurIPS Continual Learning Workshop, 2018
Hierarchical gaussian process priors for bayesian neural network weights
T Karaletsos, TD Bui
arXiv preprint arXiv:2002.04033, 2020
Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
TD Bui
University of Cambridge, 2017
Annealed Importance Sampling with q-Paths
R Brekelmans, V Masrani, T Bui, F Wood, A Galstyan, GV Steeg, ...
arXiv preprint arXiv:2012.07823, 2020
Bayesian Gaussian process state-space models via Power-EP
T Bui, RE Turner, CE Rasmussen
ICML 2016 Workshop on Data efficient Machine Learning, 2016
Importance weighted autoencoders with random neural network parameters
D Hernández-Lobato, TD Bui, Y Li, JM Hernández-Lobato, RE Turner
Workshop on Bayesian Deep Learning, NIPS 2016, 2016
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