Thang D. Bui - RankMe

36 papers A* 4A 1Journal 25Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Active Flow Matching. CoRR Yashvir S. Grewal, Daniel M. Steinberg, Thang D. Bui, Cheng Soon Ong, Edwin V. Bonilla
2026	J	`jnl`	Beyond ReinMax: Low-Variance Gradient Estimators for Discrete Latent Variables. CoRR Daniel Wang, Thang D. Bui
2025	J	`jnl`	Sparks of Science: Hypothesis Generation Using Structured Paper Data. CoRR Charles O'Neill, Tirthankar Ghosal, Roberta Raileanu, Mike Walmsley, Thang D. Bui, Kevin Schawinski, Ioana Ciuca
2025	J	`jnl`	Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited. CoRR Thang D. Bui, Michalis K. Titsias
2025	J	`jnl`	Tighter sparse variational Gaussian processes. CoRR Thang D. Bui, Matthew Ashman, Richard E. Turner
2025	J	`jnl`	Tighter sparse variational Gaussian processes. Trans. Mach. Learn. Res. Thang D. Bui, Matthew Ashman, Richard E. Turner
2024	J	`jnl`	Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity. Trans. Mach. Learn. Res. Jack W. Miller, Charles O'Neill, Thang D. Bui
2024	J	`jnl`	Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings. CoRR Yashvir S. Grewal, Edwin V. Bonilla, Thang D. Bui
2024	J	`jnl`	Likelihood approximations via Gaussian approximate inference. CoRR Thang D. Bui
2024	J	`jnl`	Measuring Sharpness in Grokking. CoRR Jack W. Miller, Patrick J. Gleeson, Charles O'Neill, Thang D. Bui, Noam Levi
2024	J	`jnl`	Sparse Autoencoders Enable Scalable and Reliable Circuit Identification in Language Models. CoRR Charles O'Neill, Thang D. Bui
2023	J	`jnl`	Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content. CoRR Charles O'Neill, Jack W. Miller, Ioana Ciuca, Yuan-Sen Ting, Thang D. Bui
2023	J	`jnl`	AstroLLaMA: Towards Specialized Foundation Models in Astronomy. CoRR 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
2023	J	`jnl`	Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity. CoRR Jack W. Miller, Charles O'Neill, Thang D. Bui
2023	J	`jnl`	Steering Language Generation: Harnessing Contrastive Expert Guidance and Negative Prompting for Coherent and Diverse Synthetic Data Generation. CoRR Charles O'Neill, Yuan-Sen Ting, Ioana Ciuca, Jack W. Miller, Thang D. Bui
2022	J	`jnl`	Partitioned Variational Inference: A Framework for Probabilistic Federated Learning. CoRR Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner
2021	A*	`conf`	Variational Auto-Regressive Gaussian Processes for Continual Learning. ICML Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui
2020	A*	`conf`	Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. NeurIPS Theofanis Karaletsos, Thang D. Bui
2020	J	`jnl`	Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. CoRR Theofanis Karaletsos, Thang D. Bui
2020	—	`ed.`	Symposium on Advances in Approximate Bayesian Inference, AABI 2019, Vancouver, BC, Canada, December 8, 2019. AABI Cheng Zhang, Francisco J. R. Ruiz, Thang D. Bui, Adji Bousso Dieng, Dawen Liang
2020	J	`jnl`	Variational Auto-Regressive Gaussian Processes for Continual Learning. CoRR Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui
2019	J	`jnl`	Improving and Understanding Variational Continual Learning. CoRR Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner
2019	—	`ed.`	Symposium on Advances in Approximate Bayesian Inference, AABI 2018, Montréal, QC, Canada, December 2, 2018. AABI Francisco J. R. Ruiz, Cheng Zhang, Dawen Liang, Thang D. Bui
2018	A	`conf`	Neural Graph Learning: Training Neural Networks Using Graphs. WSDM Thang D. Bui, Sujith Ravi, Vivek Ramavajjala
2018	J	`jnl`	Partitioned Variational Inference: A unified framework encompassing federated and continual learning. CoRR Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner
2018	—	`conf`	Variational Continual Learning. ICLR (Poster) Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
2017	J	`jnl`	A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation. J. Mach. Learn. Res. Thang D. Bui, Josiah Yan, Richard E. Turner
2017	J	`jnl`	Neural Graph Machines: Learning Neural Networks Using Graphs. CoRR Thang D. Bui, Sujith Ravi, Vivek Ramavajjala
2017	—	`conf`	Streaming Sparse Gaussian Process Approximations. NIPS Thang D. Bui, Cuong V. Nguyen, Richard E. Turner
2017	J	`jnl`	Variational Continual Learning. CoRR Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
2016	J	`jnl`	A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation. CoRR Thang D. Bui, Josiah Yan, Richard E. Turner
2016	A*	`conf`	Black-Box Alpha Divergence Minimization. ICML José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner
2016	A*	`conf`	Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner
2016	J	`jnl`	Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner
2015	—	`conf`	Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. NIPS Felipe A. Tobar, Thang D. Bui, Richard E. Turner
2014	—	`conf`	Tree-structured Gaussian Process Approximations. NIPS Thang D. Bui, Richard E. Turner