Thang D Bui
Thang D Bui
Research Scientist, Uber AI; Lecturer, University of Sydney
Verified email at sydney.edu.au - Homepage
TitleCited byYear
Variational continual learning
CV Nguyen, Y Li, TD Bui, RE Turner
International Conference on Learning Representations (ICLR), 2018
952018
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
892016
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
882016
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
38*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
372015
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
23*2018
Streaming sparse Gaussian process approximations
TD Bui, C Nguyen, RE Turner
Advances in Neural Information Processing Systems, 3299-3307, 2017
212017
Design of covariance functions using inter-domain inducing variables
F Tobar, TD Bui, RE Turner
NIPS Time Series Workshop, 2015
92015
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
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
Improving and Understanding Variational Continual Learning
S Swaroop, CV Nguyen, TD Bui, RE Turner
arXiv preprint arXiv:1905.02099, 2019
22019
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
22018
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
22017
Natural Variational Continual Learning
H Tseran, ME Khan, T Harada, TD Bui
NeurIPS Continual Learning Workshop, 2018
12018
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
Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
TD Bui
University of Cambridge, 2017
2017
Variational Continual Learning in Deep Models
CV Nguyen, Y Li, TD Bui, RE Turner
NIPS Bayesian Deep Learning Workshop, 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
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Articles 1–20