Thang D Bui
Thang D Bui
Research Scientist, Uber AI; Lecturer, University of Sydney
Verified email at - Homepage
Cited by
Cited by
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, TD Bui, RE Turner
Advances in Neural Information Processing Systems, 3501-3509, 2015
Streaming sparse Gaussian process approximations
TD Bui, C Nguyen, RE Turner
Advances in Neural Information Processing Systems 30, 3299-3307, 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
Stochastic variational inference for Gaussian process latent variable models using back constraints
TD Bui, RE Turner
Black Box Learning and Inference NIPS workshop, 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
Natural Variational Continual Learning
H Tseran, ME Khan, T Harada, TD Bui
NeurIPS Continual Learning Workshop, 2018
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
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
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
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
D Hernández-Lobato, JM Hernández-Lobato, Y Li, T Bui, RE Turner
arXiv preprint arXiv:1511.03249, 2015
An introduction to Sequential Monte Carlo
TBJ Frellsen, T Bui
Cambridge, 2014
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