Bin Yu
- Artificial Intelligence top 0.5%
- Statistics and Probability top 0.2%
- Molecular Biology top 10%
- Computer Vision and Pattern Recognition top 2%
- Astronomy and Astrophysics top 5%
- Co-authors
- Peter BühlmannKarl KumbierChandan SinghWilliam J. MurdochReza Abbasi-AslMark HansenJ. RissanenAndrew R. Barron
- Topics
- Machine Learning and Algorithms (11 papers)Bayesian Methods and Mixture Models (11 papers)Algorithms and Data Compression (10 papers)
- Journals
- Proceedings of the National Academy of SciencesSHILAP Revista de lepidopterologíaJournal of Geophysical Research Atmospheres
- Partner nations
- United StatesChinaGermany
In The Last Decade
Bin Yu
80 papers receiving 5.5k citations
Hit Papers
Peers
Comparison fields: 5 of 204
- Artificial Intelligence 2.3k
- Statistics and Probability 1.0k
- Molecular Biology 858
- Computer Vision and Pattern Recognition 607
- Astronomy and Astrophysics 548
Countries citing papers authored by Bin Yu
This map shows the geographic impact of Bin Yu'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 Bin Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bin Yu more than expected).
Fields of papers citing papers by Bin Yu
This network shows the impact of papers produced by Bin Yu. 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 Bin Yu. The network helps show where Bin Yu may publish in the future.
Co-authorship network of co-authors of Bin Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Bin Yu. A scholar is included among the top collaborators of Bin Yu 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 Bin Yu. Bin Yu 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 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 15 | |
| 11 | 4 | |
| 12 | 5 | |
| 13 | Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge | 32 |
| 14 | 191 | |
| 15 | Hierarchical interpretations for neural network predictions | 16 |
| 16 | Local Identifiability of $\ell_1$-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition | 2 |
| 17 | Supervised Neighborhoods for Distributed Nonparametric Regression | 4 |
| 18 | Counting and exploring sizes of Markov equivalence classes of directed acyclic graphs | 13 |
| 19 | 5 | |
| 20 | 139 |
About Bin Yu
Bin Yu is a scholar working on Statistics and Probability, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 90 papers that have together received 5.9k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (11 papers), Bayesian Methods and Mixture Models (11 papers) and Algorithms and Data Compression (10 papers). The work is most often cited by research in Statistics and Probability (1.0k citations), Health Informatics (106 citations) and Artificial Intelligence (2.3k citations). Bin Yu has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Peter Bühlmann, Karl Kumbier, Chandan Singh, William J. Murdoch, Reza Abbasi-Asl, Mark Hansen, J. Rissanen, Andrew R. Barron, Martin J. Wainwright and Per A. Mykland. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Journal of Geophysical Research Atmospheres.
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.