Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Using probabilistic generative models for ranking risks of Android apps
2012260 citationsHao Peng, Chris Gates et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Yuan Qi'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 Yuan Qi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuan Qi more than expected).
This network shows the impact of papers produced by Yuan Qi. 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 Yuan Qi. The network helps show where Yuan Qi may publish in the future.
Co-authorship network of co-authors of Yuan Qi
This figure shows the co-authorship network connecting the top 25 collaborators of Yuan Qi.
A scholar is included among the top collaborators of Yuan Qi 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 Yuan Qi. Yuan Qi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhe, Shandian, et al.. (2017). Asynchronous Distributed Variational Gaussian Process for Regression. International Conference on Machine Learning. 2788–2797.9 indexed citations
8.
Qi, Yuan & Yandong Guo. (2013). Message passing with l 1 penalized KL minimization. International Conference on Machine Learning. 262–270.3 indexed citations
9.
Peng, Hao, Chris Gates, Ninghui Li, et al.. (2013). Using probabilistic generative models for ranking risks of android apps. 25.1 indexed citations
Qi, Yuan, et al.. (2010). Sparse-posterior Gaussian processes for general likelihoods. Uncertainty in Artificial Intelligence. 450–457.11 indexed citations
12.
Qi, Yuan. (2010). Automatic extraction and alignment of multiword expressions from English-Chinese comparable corpus. Computer Engineering and Applications Journal.1 indexed citations
13.
Yan, Feng & Yuan Qi. (2010). Sparse Gaussian Process Regression via L1 Penalization. International Conference on Machine Learning. 1183–1190.16 indexed citations
14.
Ding, Nan, Yuan Qi, Rongjing Xiang, Ian Molloy, & Ninghui Li. (2010). Nonparametric Bayesian Matrix Factorization by Power-EP. International Conference on Artificial Intelligence and Statistics. 169–176.12 indexed citations
15.
Feng, Yan, S. V. N. Vishwanathan, & Yuan Qi. (2010). Cooperative Autonomous Online Learning. arXiv (Cornell University).3 indexed citations
16.
Qi, Yuan. (2009). Research on approaches of syntactic reordering for statistical machine translation. Computer Engineering and Applications Journal.1 indexed citations
17.
Yan, Feng, Ningyi Xu, & Yuan Qi. (2009). Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units. Neural Information Processing Systems. 22. 2134–2142.53 indexed citations
18.
Qi, Yuan. (2008). Research on real-time dim target detection algorithm in deep space. Optical Technique.
19.
Qi, Yuan & Tom Minka. (2003). Tree-structured Approximations by Expectation Propagation. Neural Information Processing Systems. 16. 193–200.44 indexed citations
20.
Qi, Yuan. (2002). Effects of oscillation operations on plasmid stability of S. cerevisiae harboring plasmid. Journal of Beijing University of Chemical Technology.1 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.