T. Bui

发表

Black-box α-divergence minimization

Richard E. Turner, José Miguel Hernández-Lobato, Mark Rowland, 2016, ICML 2016.

Stochastic Variational Inference for Gaussian Process Latent Variable Models using Back Constraints

Richard E. Turner, T. Bui, 2015 .

Sparse Approximations for Non-Conjugate Gaussian Process Regression

Richard E. Turner, T. Bui, 2014 .

M L ] 1 0 N ov 2 01 5 Black-box α-divergence Minimization

Richard E. Turner, José Miguel Hernández-Lobato, Yingzhen Li, 2015 .

Circular Pseudo-Point Approximations for Scaling Gaussian Processes

Richard E. Turner, T. Bui, Will Tebbutt, 2016 .

Variational Continual Learning pdf

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

Neural network analysis of sparse datasets ?? an application to the fracture system in folds of the Lisburne Formation, northeastern Alaska

T. Bui, 2005 .

Variational Continual Learning in Deep Models

Richard E. Turner, Cuong V Nguyen, Yingzhen Li, 2017 .

Variational Bayesian Inference : Algorithms for Sparse Gaussian Processes and Theoretical Bounds

Richard E. Turner, Cuong V Nguyen, Yingzhen Li, 2017 .

Biases in Variational Bayesian Neural Networks

T. Bui, 2021 .

Stochastic Expectation Propagation for Large Scale Gaussian Process Classification

Richard E. Turner, José Miguel Hernández-Lobato, Yingzhen Li, 2015, 1511.03249.

The Influence of Fold and Fracture Development on Reservoir Behavior of the Lisburne Group of Northern Alaska

T. Bui, T. Parris, C. Hanks, 2000 .

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

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

q-Paths: Generalizing the Geometric Annealing Path using Power Means

Frank Nielsen, Frank Wood, Vaden Masrani, 2021, UAI.

Project Title : Interwell Connectivity and Diagnosis Using Correlation of Production and Injection Rate Data in Hydrocarbon Production ANNUAL REPORT Reporting Period Start

T. Bui, L. Lake, A. Al-yousef, 2004 .

Black-Box Alpha Divergence Minimization

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

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights pdf

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

Variational Auto-Regressive Gaussian Processes for Continual Learning

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

Improving and Understanding Variational Continual Learning pdf

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

Neural Graph Learning: Training Neural Networks Using Graphs

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

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..

Character, relative age and implications of fractures and other mesoscopic structures associated with detachment folds: an example from the Lisburne Group of the northeastern Brooks Range, Alaska

T. Bui, C. Hanks, J. Lorenz, 2003 .

Partitioned Variational Inference: A Framework for Probabilistic Federated Learning

Richard E. Turner, Cuong V Nguyen, T. Bui, 2022, ArXiv.

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

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

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

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

Tree-structured Gaussian Process Approximations

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

Streaming Sparse Gaussian Process Approximations

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

Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models

Thang D. Bui, T. Bui, 2018 .

Annealed Importance Sampling with q-Paths pdf

Frank Nielsen, Frank D. Wood, Vaden Masrani, 2020, ArXiv.

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels

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

Black-Box Alpha Divergence Minimization

Richard E. Turner, José Miguel Hernández-Lobato, Mark Rowland, 2016, ICML.

Neural Network Modeling with Sparse Datasets

Thang Bui, J. L. Jensen, C. L. Hanks, 2008 .