Thang D. Bui

发表

Neural Graph Learning: Training Neural Networks Using Graphs

Thang D. Bui, Sujith Ravi, Vivek Ramavajjala, 2018, WSDM.

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels

Richard E. Turner, Thang D. Bui, Felipe A. Tobar, 2015, NIPS.

Streaming Sparse Gaussian Process Approximations

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

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

Richard E. Turner, Thang D. Bui, Cuong V. Nguyen, 2018, ArXiv.

Variational Continual Learning pdf

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

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Daniel Hernández-Lobato, Richard E. Turner, Thang D. Bui, 2016, ICML.

Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation

Daniel Hern'andez-Lobato, Richard E. Turner, Jos'e Miguel Hern'andez-Lobato, 2018 .

Gaussian Process Meta-Representations Of Neural Networks

Thang D. Bui, Theofanis Karaletsos, 2019 .

Neural Graph Machines: Learning Neural Networks Using Graphs pdf

Thang D. Bui, Sujith Ravi, Vivek Ramavajjala, 2017, ArXiv.

Improving and Understanding Variational Continual Learning pdf

Richard E. Turner, Thang D. Bui, Cuong V. Nguyen, 2019, ArXiv.

Variational Auto-Regressive Gaussian Processes for Continual Learning pdf

Thang D. Bui, Theofanis Karaletsos, Sanyam Kapoor, 2020, ArXiv.

Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models

Thang D. Bui, T. Bui, 2018 .

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights pdf

Thang D. Bui, Theofanis Karaletsos, T. Bui, 2020, NeurIPS.

Tree-structured Gaussian Process Approximations

Richard E. Turner, Thang D. Bui, T. Bui, 2014, NIPS.

Black-Box Alpha Divergence Minimization

Daniel Hernández-Lobato, Richard E. Turner, Thang D. Bui, 2015, ICML.

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

Richard E. Turner, Thang D. Bui, Josiah Yan, 2016, ArXiv.

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

Richard E. Turner, Thang D. Bui, Josiah Yan, 2016, J. Mach. Learn. Res..

Variational Auto-Regressive Gaussian Processes for Continual Learning

Thang D. Bui, Theofanis Karaletsos, Sanyam Kapoor, 2020, ICML.

Design of Covariance Functions using Inter-Domain Inducing Variables

Thang D. Bui, Felipe Tobar, 2015 .