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Variational Auto-Regressive Gaussian Processes for Continual Learning


Jun 09, 2020
Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui

* Preprint. Under review 

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Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights


Feb 10, 2020
Theofanis Karaletsos, Thang D. Bui

* 12 pages main paper, 13 pages appendix 

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Improving and Understanding Variational Continual Learning


May 06, 2019
Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner


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Partitioned Variational Inference: A unified framework encompassing federated and continual learning


Nov 27, 2018
Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner


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Variational Continual Learning


May 20, 2018
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner

* Published at International Conference on Learning Representations (ICLR) 2018 

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Streaming Sparse Gaussian Process Approximations


Nov 12, 2017
Thang D. Bui, Cuong V. Nguyen, Richard E. Turner

* To appear at the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. The first two authors contributed equally to this work 

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A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation


Oct 05, 2017
Thang D. Bui, Josiah Yan, Richard E. Turner


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Neural Graph Machines: Learning Neural Networks Using Graphs


Mar 14, 2017
Thang D. Bui, Sujith Ravi, Vivek Ramavajjala

* 9 pages 

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Deep Gaussian Processes for Regression using Approximate Expectation Propagation


Feb 12, 2016
Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner


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Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation


Nov 11, 2015
Thang D. Bui, José Miguel Hernández-Lobato, Yingzhen Li, Daniel Hernández-Lobato, Richard E. Turner

* accepted to Workshop on Advances in Approximate Bayesian Inference, NIPS 2015 

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