Thang D. Bui

1.3k total citations
9 papers, 226 citations indexed

About

Thang D. Bui is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Thang D. Bui has authored 9 papers receiving a total of 226 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 3 papers in Control and Systems Engineering and 2 papers in Computational Theory and Mathematics. Recurrent topics in Thang D. Bui's work include Gaussian Processes and Bayesian Inference (7 papers), Control Systems and Identification (3 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). Thang D. Bui is often cited by papers focused on Gaussian Processes and Bayesian Inference (7 papers), Control Systems and Identification (3 papers) and Advanced Multi-Objective Optimization Algorithms (2 papers). Thang D. Bui collaborates with scholars based in United Kingdom, United States and Spain. Thang D. Bui's co-authors include Richard E. Turner, Yingzhen Li, Sujith Ravi, Vivek Ramavajjala, Cuong V. Nguyen, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Felipe Tobar, Theofanis Karaletsos and Mark Rowland and has published in prestigious journals such as Journal of Machine Learning Research, Apollo (University of Cambridge) and Neural Information Processing Systems.

In The Last Decade

Thang D. Bui

9 papers receiving 214 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Thang D. Bui United Kingdom	8	175	56	36	34	17	9	226
Luana Ruiz United States	7	137 0.8×	63 1.1×	27 0.8×	20 0.6×	14 0.8×	25	251
M. Gethsiyal Augasta India	7	195 1.1×	56 1.0×	22 0.6×	24 0.7×	16 0.9×	11	271
Mathias Berglund Finland	5	137 0.8×	80 1.4×	11 0.3×	16 0.5×	32 1.9×	5	247
Jason Liang United States	7	118 0.7×	85 1.5×	18 0.5×	12 0.4×	14 0.8×	9	259
Pavel Kordík Czechia	8	105 0.6×	36 0.6×	17 0.5×	10 0.3×	19 1.1×	32	216
Li-Lun Wang United States	5	142 0.8×	151 2.7×	16 0.4×	34 1.0×	17 1.0×	7	284
Yongmin Lin China	5	266 1.5×	74 1.3×	23 0.6×	8 0.2×	14 0.8×	11	364
Thorsten Suttorp Germany	3	145 0.8×	27 0.5×	80 2.2×	16 0.5×	11 0.6×	5	200
Justin Domke United States	9	128 0.7×	157 2.8×	12 0.3×	8 0.2×	10 0.6×	24	306

Countries citing papers authored by Thang D. Bui

Since Specialization

Citations

This map shows the geographic impact of Thang D. Bui's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thang D. Bui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thang D. Bui more than expected).

Fields of papers citing papers by Thang D. Bui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thang D. Bui. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thang D. Bui. The network helps show where Thang D. Bui may publish in the future.

Co-authorship network of co-authors of Thang D. Bui

This figure shows the co-authorship network connecting the top 25 collaborators of Thang D. Bui. A scholar is included among the top collaborators of Thang D. Bui based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thang D. Bui. Thang D. Bui is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Karaletsos, Theofanis & Thang D. Bui. (2020). Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights. Neural Information Processing Systems. 33. 17141–17152. 3 indexed citations

2.

Nguyen, Cuong V., Thang D. Bui, Yingzhen Li, & Richard E. Turner. (2019). Variational continual learning. Apollo (University of Cambridge). 36 indexed citations

3.

Bui, Thang D., et al.. (2018). A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation. Journal of Machine Learning Research. 18(104). 1–72. 36 indexed citations

4.

Bui, Thang D., Sujith Ravi, & Vivek Ramavajjala. (2018). Neural Graph Learning. 64–71. 51 indexed citations

5.

Bui, Thang D., Cuong V. Nguyen, & Richard E. Turner. (2017). Streaming sparse Gaussian process approximations. Apollo (University of Cambridge). 30. 3299–3307. 20 indexed citations

6.

Hernández-Lobato, José Miguel, Yingzhen Li, Mark Rowland, et al.. (2016). Black-Box α-divergence minimization. Apollo (University of Cambridge). 8 indexed citations

7.

Bui, Thang D., José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Yingzhen Li, & Richard E. Turner. (2016). Deep Gaussian Processes for Regression using Approximate Expectation Propagation. Apollo (University of Cambridge). 38 indexed citations

8.

Tobar, Felipe, Thang D. Bui, & Richard E. Turner. (2015). Learning stationary time series using Gaussian processes with nonparametric kernels. Apollo (University of Cambridge). 18 indexed citations

9.

Bui, Thang D. & Richard E. Turner. (2014). Tree-structured Gaussian Process Approximations. Neural Information Processing Systems. 27. 2213–2221. 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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