Thang D. Bui

According to our database, Thang D. Bui authored at least 15 papers between 2014 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Partitioned Variational Inference: A Framework for Probabilistic Federated Learning.

Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner

CoRR, 2022

2021

Variational Auto-Regressive Gaussian Processes for Continual Learning.

Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights.

Theofanis Karaletsos, Thang D. Bui

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Improving and Understanding Variational Continual Learning.

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

CoRR, 2019

2018

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

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

CoRR, 2018

Neural Graph Learning: Training Neural Networks Using Graphs.

Thang D. Bui, Sujith Ravi, Vivek Ramavajjala

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018

Variational Continual Learning.

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

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

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

Thang D. Bui, Josiah Yan, Richard E. Turner

J. Mach. Learn. Res., 2017

Neural Graph Machines: Learning Neural Networks Using Graphs.

Thang D. Bui, Sujith Ravi, Vivek Ramavajjala

CoRR, 2017

Streaming Sparse Gaussian Process Approximations.

Thang D. Bui, Cuong V. Nguyen, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation.

Thang D. Bui, Josiah Yan, Richard E. Turner

CoRR, 2016

Black-Box Alpha Divergence Minimization.

José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner

Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels.

Felipe A. Tobar, Thang D. Bui, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Tree-structured Gaussian Process Approximations.

Thang D. Bui, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014