Follow
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
4902018
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
2182016
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
2022016
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
1342017
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
992018
Streaming sparse Gaussian process approximations
TD Bui, CV Nguyen, RE Turner
Advances in Neural Information Processing Systems 30 (NeurIPS), 2017
722017
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
722015
Tree-structured Gaussian Process Approximations
TD Bui, RE Turner
Advances in Neural Information Processing Systems, 2213-2221, 2014
512014
Improving and understanding variational continual learning
S Swaroop, CV Nguyen, TD Bui, RE Turner
arXiv preprint arXiv:1905.02099, 2019
432019
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
322018
Hierarchical Gaussian process priors for Bayesian neural network weights
T Karaletsos, TD Bui
Advances in Neural Information Processing Systems 33 (NeurIPS), 2020
19*2020
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
Stochastic variational inference for Gaussian process latent variable models using back constraints
TD Bui, RE Turner
Black Box Learning and Inference NIPS workshop, 2015
132015
Natural Variational Continual Learning
H Tseran, ME Khan, T Harada, TD Bui
NeurIPS Continual Learning Workshop, 2018
122018
Design of covariance functions using inter-domain inducing variables
F Tobar, TD Bui, RE Turner
NIPS Time Series Workshop, 2015
122015
Variational auto-regressive gaussian processes for continual learning
S Kapoor, T Karaletsos, TD Bui
International Conference on Machine Learning, 5290-5300, 2021
82021
Annealed importance sampling with q-paths
R Brekelmans, V Masrani, T Bui, F Wood, A Galstyan, GV Steeg, ...
arXiv preprint arXiv:2012.07823, 2020
82020
Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models
TD Bui
University of Cambridge, 2017
62017
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
62017
q-Paths: Generalizing the Geometric Annealing Path using Power Means
V Masrani, R Brekelmans, T Bui, F Nielsen, A Galstyan, GV Steeg, ...
37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021
42021
The system can't perform the operation now. Try again later.
Articles 1–20