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ORCIDThang D. Bui
Person information
- affiliation: University of Cambridge, Department of Engineering, UK
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Journal Articles
- 2017
[j1]Thang D. Bui, Josiah Yan, Richard E. Turner:
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation. J. Mach. Learn. Res. 18: 104:1-104:72 (2017)
Conference and Workshop Papers
- 2021
- [c9]Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui:
Variational Auto-Regressive Gaussian Processes for Continual Learning. ICML 2021: 5290-5300 - 2020
- [c8]Theofanis Karaletsos, Thang D. Bui:
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. NeurIPS 2020 - 2018
- [c7]Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner:
Variational Continual Learning. ICLR (Poster) 2018 - [c6]Thang D. Bui
, Sujith Ravi, Vivek Ramavajjala:
Neural Graph Learning: Training Neural Networks Using Graphs. WSDM 2018: 64-71 - 2017
- [c5]Thang D. Bui, Cuong V. Nguyen, Richard E. Turner:
Streaming Sparse Gaussian Process Approximations. NIPS 2017: 3299-3307 - 2016
- [c4]Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. ICML 2016: 1472-1481 - [c3]José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Thang D. Bui, Daniel Hernández-Lobato, Richard E. Turner:
Black-Box Alpha Divergence Minimization. ICML 2016: 1511-1520 - 2015
- [c2]Felipe A. Tobar, Thang D. Bui, Richard E. Turner:
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels. NIPS 2015: 3501-3509 - 2014
- [c1]Thang D. Bui, Richard E. Turner:
Tree-structured Gaussian Process Approximations. NIPS 2014: 2213-2221
Editorship
- 2020
- [e2]Cheng Zhang, Francisco J. R. Ruiz, Thang D. Bui, Adji Bousso Dieng, Dawen Liang:
Symposium on Advances in Approximate Bayesian Inference, AABI 2019, Vancouver, BC, Canada, December 8, 2019. Proceedings of Machine Learning Research 118, PMLR 2020 [contents] - 2019
- [e1]Francisco J. R. Ruiz, Cheng Zhang, Dawen Liang, Thang D. Bui:
Symposium on Advances in Approximate Bayesian Inference, AABI 2018, Montréal, QC, Canada, December 2, 2018. Proceedings of Machine Learning Research 96, PMLR 2019 [contents]
Informal and Other Publications
- 2022
- [i9]Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Efstratios Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner:
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning. CoRR abs/2202.12275 (2022) - 2020
- [i8]Theofanis Karaletsos, Thang D. Bui:
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. CoRR abs/2002.04033 (2020) - [i7]Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui:
Variational Auto-Regressive Gaussian Processes for Continual Learning. CoRR abs/2006.05468 (2020) - 2019
- [i6]Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner:
Improving and Understanding Variational Continual Learning. CoRR abs/1905.02099 (2019) - 2018
- [i5]Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner:
Partitioned Variational Inference: A unified framework encompassing federated and continual learning. CoRR abs/1811.11206 (2018) - 2017
- [i4]Thang D. Bui, Sujith Ravi, Vivek Ramavajjala:
Neural Graph Machines: Learning Neural Networks Using Graphs. CoRR abs/1703.04818 (2017) - [i3]Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner:
Variational Continual Learning. CoRR abs/1710.10628 (2017) - 2016
- [i2]Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner:
Deep Gaussian Processes for Regression using Approximate Expectation Propagation. CoRR abs/1602.04133 (2016) - [i1]Thang D. Bui, Josiah Yan, Richard E. Turner:
A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation. CoRR abs/1605.07066 (2016)
Coauthor Index
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