フォロー
Thang D Bui
タイトル
引用先
引用先
Variational continual learning
CV Nguyen, Y Li, TD Bui, RE Turner
International Conference on Learning Representations (ICLR), 2018
8592018
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
2832016
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
2742016
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
2012017
Streaming sparse Gaussian process approximations
TD Bui, CV Nguyen, RE Turner
Advances in Neural Information Processing Systems 30 (NeurIPS), 2017
1382017
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
135*2018
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
1062015
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
712018
Improving and understanding variational continual learning
S Swaroop, CV Nguyen, TD Bui, RE Turner
arXiv preprint arXiv:1905.02099, 2019
632019
Tree-structured Gaussian Process Approximations
TD Bui, RE Turner
Advances in Neural Information Processing Systems, 2213-2221, 2014
612014
Hierarchical Gaussian process priors for Bayesian neural network weights
T Karaletsos, TD Bui
Advances in Neural Information Processing Systems 33 (NeurIPS), 2020
352020
Variational auto-regressive Gaussian processes for continual learning
S Kapoor, T Karaletsos, TD Bui
International Conference on Machine Learning, 5290-5300, 2021
332021
Stochastic variational inference for Gaussian process latent variable models using back constraints
TD Bui, RE Turner
Black Box Learning and Inference NIPS workshop, 2015
222015
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
192021
Natural Variational Continual Learning
H Tseran, ME Khan, T Harada, TD Bui
NeurIPS Continual Learning Workshop, 2018
192018
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
182015
Astrollama: Towards specialized foundation models in astronomy
TD Nguyen, YS Ting, I Ciucă, C O'Neill, ZC Sun, M Jabłońska, S Kruk, ...
arXiv preprint arXiv:2309.06126, 2023
152023
Design of covariance functions using inter-domain inducing variables
F Tobar, TD Bui, RE Turner
NIPS Time Series Workshop, 2015
142015
Partitioned variational inference: A framework for probabilistic federated learning
M Ashman, TD Bui, CV Nguyen, S Markou, A Weller, S Swaroop, ...
arXiv preprint arXiv:2202.12275, 2022
132022
Annealed importance sampling with q-paths
R Brekelmans, V Masrani, T Bui, F Wood, A Galstyan, GV Steeg, ...
arXiv preprint arXiv:2012.07823, 2020
132020
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