Yun Yang
- Statistics and Probability top 1%
- Artificial Intelligence top 5%
- Computational Mechanics top 10%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Michael I. JordanJason D. LeeMartin J. WainwrightAntonio R. LineroDavid B. DunsonDebdeep PatiAnirban BhattacharyaMert Pilancı
- Topics
- Statistical Methods and Inference (15 papers)Bayesian Methods and Mixture Models (10 papers)Sparse and Compressive Sensing Techniques (6 papers)
- Journals
- Journal of the American Statistical AssociationPLoS ONEIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Yun Yang
35 papers receiving 876 citations
Peers
Comparison fields: 5 of 133
- Statistics and Probability 406
- Artificial Intelligence 391
- Computational Mechanics 125
- Computer Vision and Pattern Recognition 86
- Computer Networks and Communications 84
Countries citing papers authored by Yun Yang
This map shows the geographic impact of Yun Yang'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 Yun Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yun Yang more than expected).
Fields of papers citing papers by Yun Yang
This network shows the impact of papers produced by Yun Yang. 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 Yun Yang. The network helps show where Yun Yang may publish in the future.
Co-authorship network of co-authors of Yun Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Yun Yang. A scholar is included among the top collaborators of Yun Yang 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 Yun Yang. Yun Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 3 | |
| 5 | On Empirical Bayes Variational Autoencoder: An Excess Risk Bound | 1 |
| 6 | 1 | |
| 7 | 12 | |
| 8 | 9 | |
| 9 | Non-asymptotic Analysis for Nonparametric Testing | 2 |
| 10 | 30 | |
| 11 | 3 | |
| 12 | On Statistical Optimality of Variational Bayes | 4 |
| 13 | 105 | |
| 14 | 58 | |
| 15 | 39 | |
| 16 | 12 | |
| 17 | 12 | |
| 18 | 66 | |
| 19 | 34 | |
| 20 | 35 |
About Yun Yang
Yun Yang is a scholar working on Statistics and Probability, Computational Mathematics and Artificial Intelligence, having authored 39 papers that have together received 905 indexed citations. Recurring topics across this work include Statistical Methods and Inference (15 papers), Bayesian Methods and Mixture Models (10 papers) and Sparse and Compressive Sensing Techniques (6 papers). The work is most often cited by research in Statistics and Probability (406 citations), Computational Mathematics (24 citations) and Complementary and Manual Therapy (83 citations). Yun Yang has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Michael I. Jordan, Jason D. Lee, Martin J. Wainwright, Antonio R. Linero, David B. Dunson, Debdeep Pati, Anirban Bhattacharya, Mert Pilancı, Jennifer L. Robinson and Sunil Wadhwa. Their work appears in journals such as Journal of the American Statistical Association, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.