Shengbo Guo
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computational Mathematics top 2%
- Computer Vision and Pattern Recognition
- Computational Mechanics
- Co-authors
- Scott SannerEdwin V. BonillaCédric ArchambeauYingjian WangGary ChenDavid B. DunsonLawrence CarinPiyush Rai
- Topics
- Sparse and Compressive Sensing Techniques (2 papers)Information Retrieval and Search Behavior (2 papers)Topic Modeling (2 papers)
- Journals
- Machine LearningANU Open Research (Australian National University)Neural Information Processing Systems
- Partner nations
- United StatesFranceAustralia
In The Last Decade
Shengbo Guo
8 papers receiving 217 citations
Peers
Comparison fields: 5 of 51
- Artificial Intelligence 124
- Information Systems 60
- Computational Mathematics 55
- Computer Vision and Pattern Recognition 40
- Computational Mechanics 29
Countries citing papers authored by Shengbo Guo
This map shows the geographic impact of Shengbo Guo'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 Shengbo Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shengbo Guo more than expected).
Fields of papers citing papers by Shengbo Guo
This network shows the impact of papers produced by Shengbo Guo. 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 Shengbo Guo. The network helps show where Shengbo Guo may publish in the future.
Co-authorship network of co-authors of Shengbo Guo
This figure shows the co-authorship network connecting the top 25 collaborators of Shengbo Guo. A scholar is included among the top collaborators of Shengbo Guo 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 Shengbo Guo. Shengbo Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors | 68 |
| 2 | 12 | |
| 3 | 17 | |
| 4 | Sparse Bayesian Multi-Task Learning | 23 |
| 5 | 11 | |
| 6 | Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries | 25 |
| 7 | Gaussian Process Preference Elicitation | 38 |
| 8 | 33 |
About Shengbo Guo
Shengbo Guo is a scholar working on Computational Mathematics, Signal Processing and Artificial Intelligence, having authored 8 papers that have together received 227 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (2 papers), Information Retrieval and Search Behavior (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Computational Mathematics (55 citations), Artificial Intelligence (124 citations) and Information Systems (60 citations). Shengbo Guo has collaborated with scholars based in United States, France and Australia. Frequent co-authors include Scott Sanner, Edwin V. Bonilla, Cédric Archambeau, Yingjian Wang, Gary Chen, David B. Dunson, Lawrence Carin, Piyush Rai, Onno Zoeter and Guillaume Bouchard. Their work appears in journals such as Machine Learning, ANU Open Research (Australian National University) and Neural Information Processing Systems.
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.