Thang D Bui
Thang D Bui
Research Scientist, Uber AI; Lecturer, University of Sydney
Verified email at sydney.edu.au - Homepage
TitleCited byYear
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
842016
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
802016
Variational continual learning
CV Nguyen, Y Li, TD Bui, RE Turner
International Conference on Learning Representations (ICLR), 2018
592018
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
30*2017
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
282015
Tree-structured Gaussian Process Approximations
TD Bui, RE Turner
Advances in Neural Information Processing Systems, 2213-2221, 2014
272014
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
16*2018
Streaming sparse Gaussian process approximations
TD Bui, C Nguyen, RE Turner
Advances in Neural Information Processing Systems, 3299-3307, 2017
132017
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
82015
Design of covariance functions using inter-domain inducing variables
F Tobar, TD Bui, RE Turner
NIPS 2015-Time Series Workshop, 2015
72015
Stochastic variational inference for Gaussian process latent variable models using back constraints
TD Bui, RE Turner
Black Box Learning and Inference NIPS workshop, 2015
72015
Online Variational Bayesian Inference: Algorithms for Sparse Gaussian Processes and Theoretical Bounds
CV Nguyen, TD Bui, Y Li, RE Turner
ICML Time Series Workshop, 1-5, 2017
22017
Improving and Understanding Variational Continual Learning
S Swaroop, CV Nguyen, TD Bui, RE Turner
arXiv preprint arXiv:1905.02099, 2019
2019
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
2018
Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
TD Bui
University of Cambridge, 2017
2017
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
2015
Sparse Approximations for Non-Conjugate Gaussian Process Regression
T Bui, R Turner
2014
On the paper: Variational Learning of Inducing Variables in Sparse Gaussian Processes (Titsias, 2009)
T Bui, R Turner
2014
Natural Variational Continual Learning
H Tseran, ME Khan, T Harada, TD Bui
Variational Continual Learning in Deep Models
CV Nguyen, Y Li, TD Bui, RE Turner
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Articles 1–20