Thang D. Bui

According to our database, Thang D. Bui authored at least 21 papers between 2014 and 2024.

Collaborative distances:

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

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

Sparse Autoencoders Enable Scalable and Reliable Circuit Identification in Language Models.

Charles O'Neill, Thang D. Bui

CoRR, 2024

Measuring Sharpness in Grokking.

Jack W. Miller, Patrick Gleeson, Charles O'Neill, Thang D. Bui, Noam Levi

CoRR, 2024

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, Tomasz Rózanski, Pranav Khetarpal, Sharaf Zaman, David Brodrick, Sergio J. Rodríguez Méndez, Thang D. Bui, Alyssa Goodman, Alberto Accomazzi, Jill P. Naiman, Jesse Cranney, Kevin Schawinski, UniverseTBD

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

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

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