Preprints

• Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
  Matthew Ashman, Thang Bui, Cuong Nguyen, Stratis Markou, Adrian Weller, Siddharth Swaroop, Richard Turner
  Note: An old version of this is available here.

Conference and journal papers

• Grokking beyond neural networks: An empirical exploration with model complexity
  Jack Miller, Charles O'Neill, Thang Bui
  TMLR 2024

• q-Paths: Generalizing the geometric annealing path using power means
  Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen , Aram Galstyan, Greg Ver Steeg, and Frank Wood
  UAI 2021

• Variational autoregressive Gaussian processes for continual learning
  Sanyam Kapoor, Theofanis Karaletsos, and Thang Bui
  ICML 2021
  code

• Hierarchical Gaussian process priors for Bayesian neural network weights
  Theofanis Karaletsos and Thang Bui
  NeurIPS 2020

• Variational continual learning
  Cuong Nguyen, Yingzhen Li, Thang Bui, and Rich Turner,
  ICLR 2018
  code

• Neural graph learning: Training neural networks using graphs
  Thang Bui, Sujith Ravi, and Vivek Ramavajjala
  WSDM 2018

• A unifying framework for sparse Gaussian process approximations using Power Expectation Propagation
  Thang Bui, Josiah Yan, Rich Turner
  JMLR 2017
  code

• Streaming sparse Gaussian process approximations
  Thang Bui, Cuong Nguyen, and Rich Turner
  NIPS 2017
  code

• Deep Gaussian processes for regression using approximate Expectation Propagation
  Thang Bui, José Miguel Hernández-Lobato, Yingzhen Li, Daniel Hernández-Lobato, and Rich Turner
  ICML 2016
  code

• Black-box alpha-divergence minimization
  José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang Bui, and Rich Turner
  ICML 2016
  code

• Learning stationary time series using Gaussian processes with nonparametric kernels
  Felipe Tobar, Thang Bui, and Rich Turner
  NIPS 2015 (Spotlight, acceptance rate = 3.6%)

• Tree-structured Gaussian process approximations
  Thang Bui and Rich Turner
  NIPS 2014 (Spotlight, acceptance rate = 3.6%)
  code

Thesis

• Efficient Deterministic Approximate Bayesian Inference for Gaussian Process Models
  PhD thesis
  University of Cambridge
  typos in the print version

Workshop papers

• AstroLLaMA: Towards specialised foundation models in Astronomy
  Tuan Dung Nguyen, Yuan-Sen Ting, Ioana Ciuca, et al.
  ACL Workshop on Information Extraction from Scientific Publications, 2023

• Annealed importance sampling with q-paths
  Rob Brekelmans, Vaden Masrani, Thang Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, and Frank Nielsen,
  NeurIPS workshop on Deep Learning through Information Geometry, 2020
  Best paper award

• Variational autoregressive Gaussian processes for continual learning
  Sanyam Kapoor, Theofanis Karaletsos, and Thang Bui
  ICML workshop on Continual Learning, 2020

• Gaussian process meta-representations for hierarchical neural network priors
  Theofanis Karaletsos and Thang Bui
  2nd Symposium on Advances in Approximate Bayesian Inference, 2019

• Partitioned variational inference for federated Bayesian deep learning
  Thang Bui, Cuong Nguyen, Siddharth Swaroop, and Rich Turner
  NeurIPS Bayesian Deep Learning Workshop, 2018

• Understanding and improving variational continual learning
  Siddharth Swaroop, Cuong Nguyen, Thang Bui, and Rich Turner
  NeurIPS Continual Learning Workshop, 2018

• Natural variational continual learning
  Hanna Tseran, Emtiyaz Khan, Tatsuya Harada and Thang Bui
  NeurIPS Continual Learning Workshop, 2018

• Variational continual learning for deep models
  Cuong Nguyen, Yingzhen Li, Thang Bui, and Rich Turner
  NIPS Bayesian Deep Learning Workshop, 2017

• Online variational Bayesian inference: Algorithms for sparse Gaussian processes and theoretical bounds
  Cuong Nguyen, Thang Bui, Yingzhen Li, and Rich Turner
  ICML Time Series Workshop, 2017

• Importance weighted autoencoders with random neural network parameters
  Daniel Hernández-Lobato, Thang Bui, José Miguel Hernández-Lobato, Yingzhen Li, and Rich Turner
  NIPS Workshop on Bayesian Deep Learning, 2016

• Black-box alpha divergence for generative models
  Thang Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, and Rich Turner
  NIPS Workshop on Advances in Approximate Bayesian Inference, 2016

• Circular Pseudo-point approximations for scaling Gaussian processes
  Will Tebbutt, Thang Bui and Rich Turner
  NIPS Workshop on Advances in Approximate Bayesian Inference, 2016

• Bayesian Gaussian process state space models via Power-EP
  Thang Bui, Carl Rasmussen and Rich Turner
  ICML Workshop on Data efficient Machine Learning, 2016

• Training deep Gaussian processes using stochastic expectation propagation and probabilistic backpropagation
  Thang Bui, José Miguel Hernández-Lobato, Yingzhen Li, Daniel Hernández-Lobato and Rich Turner
  NIPS Workshop on Advances in Approximate Bayesian Inference, 2015

• Stochastic variational inference for Gaussian process latent variable models using back constraints
  Thang Bui and Rich Turner
  NIPS Workshop on Black Box Learning and Inference, 2015

• Black-box alpha-divergence minimisation
  José Miguel Hernández-Lobato, Yingzhen Li, Daniel Hernández-Lobato, Thang Bui and Rich Turner
  NIPS Workshops on Advances in Approximate Bayesian Inference and Black Box Learning and Inference, 2015.

• Stochastic expectation propagation for large scale Gaussian process classification
  Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Thang Bui and Rich Turner
  NIPS Workshop on Advances in Approximate Bayesian Inference, 2015.

• Design of covariance functions using inter-domain inducing variables
  Felipe Tobar, Thang Bui and Rich Turner
  NIPS Workshop on Time Series, 2015
  Best paper award

Misc

• Connecting the Thermodynamic Variational Objective and Annealed Importance Sampling
  Thang Bui

• Sparse Approximations for Non-Conjugate Gaussian Process Regressions
  Thang Bui and Rich Turner