This map shows the geographic impact of Hung 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 Hung Bui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hung Bui more than expected).
This network shows the impact of papers produced by Hung 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 Hung Bui. The network helps show where Hung Bui may publish in the future.
Co-authorship network of co-authors of Hung Bui
This figure shows the co-authorship network connecting the top 25 collaborators of Hung Bui.
A scholar is included among the top collaborators of Hung 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 Hung Bui. Hung Bui is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Le, Trung, et al.. (2021). LAMDA: Label Matching Deep Domain Adaptation. Monash University Research Portal (Monash University). 6043–6054.7 indexed citations
2.
Dong, Zhe, Bryan Seybold, Kevin Murphy, & Hung Bui. (2020). Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems. International Conference on Machine Learning. 1. 2638–2647.1 indexed citations
Shu, Rui, et al.. (2018). A DIRT-T Approach to Unsupervised Domain Adaptation. International Conference on Learning Representations.84 indexed citations
5.
Ho, Nhat, et al.. (2017). Multilevel Clustering via Wasserstein Means. Own your potential (DEAKIN). 1501–1509.8 indexed citations
6.
Bui, Hung, Jaya Kawale, Nikos Vlassis, et al.. (2016). Practical linear models for large-scale one-class collaborative filtering. ANU Open Research (Australian National University). 3854–3860.10 indexed citations
7.
Phung, Dinh, et al.. (2016). Scalable nonparametric Bayesian multilevel clustering. Deakin Research Online (Deakin University). 289–298.3 indexed citations
Sukthankar, Gita, Christopher Geib, Hung Bui, David V. Pynadath, & Robert P. Goldman. (2014). Plan, Activity, and Intent Recognition: Theory and Practice. Journal of International Crisis and Risk Communication Research. 424–424.108 indexed citations
10.
Madani, Omid, Hung Bui, & Eric Yeh. (2009). Efficient online learning and prediction of users' desktop actions. International Joint Conference on Artificial Intelligence. 1457–1462.10 indexed citations
Bui, Hung, Dinh Phung, Svetha Venkatesh, & Hai Thanh Phan. (2008). The hidden permutation model and location-based activity recognition. Deakin Research Online (Deakin University). 1345–1350.13 indexed citations
13.
Bui, Hung, Dinh Phung, & Svetha Venkatesh. (2004). Learning Hierarchical Hidden Markov Models with General State Hierarchy.. National Conference on Artificial Intelligence. 324–329.1 indexed citations
14.
Bui, Hung, Dinh Phung, & Svetha Venkatesh. (2004). Hierarchical hidden Markov models with general state hierarchy. Deakin Research Online (Deakin University). 324–329.55 indexed citations
Bui, Hung. (2003). A general model for online probabilistic plan recognition. International Joint Conference on Artificial Intelligence. 1309–1315.128 indexed citations
17.
Venkatesh, Svetha, et al.. (2002). Coordination of multiple cameras to track multiple people. Deakin Research Online (Deakin University). 302–307.2 indexed citations
18.
Bui, Hung, et al.. (2000). On the Recognition of Abstract Markov Policies. Own your potential (DEAKIN). 524–530.9 indexed citations
19.
Bui, Hung, et al.. (1996). Negotiating agents that learn about others' preferences. Deakin Research Online (Deakin University). 16–21.2 indexed citations
20.
Bui, Hung, et al.. (1996). Learning other agents' preferences in multiagent negotiation. Deakin Research Online (Deakin University). 114–119.22 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.