Tree-structured Gaussian Process Approximations. - Thang D. Bui; Richard E. Turner (NIPS, 2014)

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. - Felipe A. Tobar; Thang D. Bui; Richard E. Turner (NIPS, 2015)

Deep Gaussian Processes for Regression using Approximate Expectation Propagation. - Thang D. Bui; Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Yingzhen Li; Richard E. Turner (ICML, 2016)

Black-Box Alpha Divergence Minimization. - José Miguel Hernández-Lobato; Yingzhen Li; Mark Rowland 001; Thang D. Bui; Daniel Hernández-Lobato; Richard E. Turner (ICML, 2016)

A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation. - Thang D. Bui; Josiah Yan; Richard E. Turner (CoRR, 2016)

Streaming Sparse Gaussian Process Approximations. - Thang D. Bui; Cuong V. Nguyen; Richard E. Turner (NIPS, 2017)

Neural Graph Machines: Learning Neural Networks Using Graphs. - Thang D. Bui; Sujith Ravi; Vivek Ramavajjala (CoRR, 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)

Variational Continual Learning. - Cuong V. Nguyen; Yingzhen Li; Thang D. Bui; Richard E. Turner (ICLR (Poster), 2018)

Neural Graph Learning: Training Neural Networks Using Graphs. - Thang D. Bui; Sujith Ravi; Vivek Ramavajjala (WSDM, 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)

Improving and Understanding Variational Continual Learning. - Siddharth Swaroop; Cuong V. Nguyen; Thang D. Bui; Richard E. Turner (CoRR, 2019)

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. - Theofanis Karaletsos; Thang D. Bui (NeurIPS, 2020)

Variational Auto-Regressive Gaussian Processes for Continual Learning. - Sanyam Kapoor; Theofanis Karaletsos; Thang D. Bui (ICML, 2021)

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)

Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generation. - Charles O'Neill; Yuan-Sen Ting; Ioana Ciuca; Jack W. Miller; Thang D. Bui (CoRR, 2023)

Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content. - Charles O'Neill; Jack W. Miller; Ioana Ciuca; Yuan-Sen Ting; Thang D. Bui (CoRR, 2023)

AstroLLaMA: Towards Specialized Foundation Models in Astronomy. - Tuan Dung Nguyen; Yuan-Sen Ting; Ioana Ciuca; Charlie O'Neill; Zechang Sun; Maja Jablonska; Sandor Kruk; Ernest Perkowski; Jack W. Miller; Jason Li; Josh Peek; Kartheik Iyer; et al. (CoRR, 2023)

Measuring Sharpness in Grokking. - Jack W. Miller; Patrick J. Gleeson; Charles O'Neill; Thang D. Bui; Noam Levi (CoRR, 2024)

Sparse Autoencoders Enable Scalable and Reliable Circuit Identification in Language Models. - Charles O'Neill; Thang D. Bui (CoRR, 2024)

Likelihood approximations via Gaussian approximate inference. - Thang D. Bui (CoRR, 2024)

Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings. - Yashvir S. Grewal; Edwin V. Bonilla; Thang D. Bui (CoRR, 2024)

Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity. - Jack W. Miller; Charles O'Neill; Thang D. Bui (Trans. Mach. Learn. Res., 2024)

Tighter sparse variational Gaussian processes. - Thang D. Bui; Matthew Ashman; Richard E. Turner (Trans. Mach. Learn. Res., 2025)

Sparks of Science: Hypothesis Generation Using Structured Paper Data. - Charles O'Neill; Tirthankar Ghosal; Roberta Raileanu; Mike Walmsley; Thang D. Bui; Kevin Schawinski; Ioana Ciuca (CoRR, 2025)

Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited. - Thang D. Bui; Michalis K. Titsias (CoRR, 2025)