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.
This map shows the geographic impact of Yi Xu'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 Yi Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yi Xu more than expected).
This network shows the impact of papers produced by Yi Xu. 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 Yi Xu. The network helps show where Yi Xu may publish in the future.
Co-authorship network of co-authors of Yi Xu
This figure shows the co-authorship network connecting the top 25 collaborators of Yi Xu.
A scholar is included among the top collaborators of Yi Xu 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 Yi Xu. Yi Xu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xu, Yi, et al.. (2018). SADAGRAD: Strongly Adaptive Stochastic Gradient Methods.. International Conference on Machine Learning. 912–920.8 indexed citations
11.
Xu, Yi, Mingrui Liu, Tianbao Yang, & Qihang Lin. (2017). No More Fixed Penalty Parameter in ADMM: Faster Convergence with New Adaptive Penalization. Neural Information Processing Systems. 1248–1258.
12.
Xu, Yi, Qihang Lin, & Tianbao Yang. (2017). Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter. Neural Information Processing Systems. 30. 3277–3287.3 indexed citations
13.
Xu, Yi, Qihang Lin, & Tianbao Yang. (2017). Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence. International Conference on Machine Learning. 3821–3830.13 indexed citations
14.
Xu, Yi, True Price, Jan‐Michael Frahm, & Fabian Monrose. (2016). Virtual U: defeating face liveness detection by building virtual models from your public photos. USENIX Security Symposium. 497–512.37 indexed citations
15.
Xu, Yi, et al.. (2012). Security and usability challenges of moving-object CAPTCHAs: decoding codewords in motion. USENIX Security Symposium. 4–4.36 indexed citations
16.
Yu, Licheng, Hongteng Xu, Yi Xu, & Xiaokang Yang. (2012). Robust single image super-resolution based on gradient enhancement. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 1–6.5 indexed citations
Yang, Xiaokang, Yi Xu, Rui Zhang, et al.. (2009). Shanghai Jiao Tong University participation in high-level feature extraction and surveillance event detection at TRECVID 2009. TRECVID.3 indexed citations
19.
Yang, Xiaokang, Rui Zhang, Yi Xu, et al.. (2008). Shanghai Jiao Tong University participation in high-level feature extraction, automatic search and surveillance event detectionat TRECVID 2008.. TRECVID.1 indexed citations
20.
Xu, Yi & Yee‐Hong Yang. (2004). Object representation using 1D displacement mapping. Graphics Interface. 33–40.1 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.