Lele Yu

513 total citations
16 papers, 360 citations indexed

About

Lele Yu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Lele Yu has authored 16 papers receiving a total of 360 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 6 papers in Information Systems. Recurrent topics in Lele Yu's work include Graph Theory and Algorithms (5 papers), Stochastic Gradient Optimization Techniques (5 papers) and Advanced Neural Network Applications (5 papers). Lele Yu is often cited by papers focused on Graph Theory and Algorithms (5 papers), Stochastic Gradient Optimization Techniques (5 papers) and Advanced Neural Network Applications (5 papers). Lele Yu collaborates with scholars based in China, Switzerland and United States. Lele Yu's co-authors include Bin Cui, Jiawei Jiang, Ce Zhang, Yingxia Shao, Jie Jiang, Yuhong Liu, Xupeng Miao, Fangcheng Fu, Yangyu Tao and Xiang Wang and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, National Science Review and ACM Transactions on Information Systems.

In The Last Decade

Lele Yu

16 papers receiving 356 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lele Yu China 9 253 138 106 103 43 16 360
Michał Podstawski Switzerland 5 172 0.7× 107 0.8× 112 1.1× 117 1.1× 49 1.1× 6 357
Yangyu Tao China 7 177 0.7× 70 0.5× 111 1.0× 139 1.3× 13 0.3× 14 316
Jike Chong United States 10 157 0.6× 75 0.5× 96 0.9× 34 0.3× 100 2.3× 25 323
Verónica Gil-Costa Argentina 10 103 0.4× 89 0.6× 191 1.8× 109 1.1× 30 0.7× 71 354
J.B. Grizzard United States 11 279 1.1× 71 0.5× 355 3.3× 151 1.5× 29 0.7× 18 491
Thomas E. Daniels United States 11 177 0.7× 34 0.2× 216 2.0× 134 1.3× 46 1.1× 24 356
Yuede Ji United States 10 160 0.6× 61 0.4× 175 1.7× 150 1.5× 18 0.4× 27 323
Jilong Xue China 11 252 1.0× 255 1.8× 238 2.2× 233 2.3× 85 2.0× 18 482
Xu Bai China 8 110 0.4× 95 0.7× 50 0.5× 110 1.1× 15 0.3× 28 304
Myungcheol Doo United States 4 96 0.4× 31 0.2× 248 2.3× 124 1.2× 36 0.8× 9 344

Countries citing papers authored by Lele Yu

Since Specialization
Citations

This map shows the geographic impact of Lele 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 Lele Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lele Yu more than expected).

Fields of papers citing papers by Lele Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lele 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 Lele Yu. The network helps show where Lele Yu may publish in the future.

Co-authorship network of co-authors of Lele Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Lele Yu. A scholar is included among the top collaborators of Lele 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 Lele Yu. Lele Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Liu, Xinyu, Wensheng Gan, Lele Yu, & Yining Liu. (2024). DP-PartFIM: Frequent Itemset Mining Using Differential Privacy and Partition. IEEE Transactions on Emerging Topics in Computing. 13(3). 567–577. 2 indexed citations
2.
Miao, Xupeng, Lingxiao Ma, Zhi Yang, et al.. (2021). CuWide: Towards Efficient Flow-based Training for Sparse Wide Models on GPUs (Extended Abstract). 2330–2331. 2 indexed citations
3.
Fu, Fangcheng, Yingxia Shao, Lele Yu, et al.. (2021). VF 2 Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning. 563–576. 40 indexed citations
4.
Jiang, Jiawei, Lele Yu, Xiao‐Sen Li, et al.. (2020). PSGraph: How Tencent trains extremely large-scale graphs with Spark?. 1549–1557. 17 indexed citations
5.
Miao, Xupeng, Lingxiao Ma, Zhi Yang, et al.. (2020). CuWide: Towards Efficient Flow-Based Training for Sparse Wide Models on GPUs. IEEE Transactions on Knowledge and Data Engineering. 34(9). 4119–4132. 9 indexed citations
6.
Wu, Wentao, et al.. (2020). C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent. 1513–1524. 3 indexed citations
7.
Cui, Bin, et al.. (2019). PS2. 376–388. 21 indexed citations
8.
Zhang, Zhipeng, Jiawei Jiang, Wentao Wu, et al.. (2019). MLlib*: Fast Training of GLMs Using Spark MLlib. 1778–1789. 14 indexed citations
9.
Shao, Yingxia, Xupeng Li, Yiru Chen, Lele Yu, & Bin Cui. (2019). Sys-TM: A Fast and General Topic Modeling System. IEEE Transactions on Knowledge and Data Engineering. 33(6). 2790–2802. 1 indexed citations
10.
Yu, Lele, et al.. (2018). GLM+: An Efficient System for Generalized Linear Models. 12. 293–300. 5 indexed citations
11.
Jiang, Jie, Lele Yu, Jiawei Jiang, Yuhong Liu, & Bin Cui. (2017). Angel: a new large-scale machine learning system. National Science Review. 5(2). 216–236. 51 indexed citations
12.
Jiang, Jiawei, et al.. (2017). GVoS. ACM Transactions on Information Systems. 36(1). 1–36. 3 indexed citations
13.
Jiang, Jiawei, Bin Cui, Ce Zhang, & Lele Yu. (2017). Heterogeneity-aware Distributed Parameter Servers. 463–478. 143 indexed citations
14.
Yu, Lele, Yingxia Shao, & Bin Cui. (2015). Exploiting Matrix Dependency for Efficient Distributed Matrix Computation. 93–105. 26 indexed citations
15.
16.
Huang, Yanxiang, Lele Yu, Xiang Wang, & Bin Cui. (2014). A multi-source integration framework for user occupation inference in social media systems. World Wide Web. 18(5). 1247–1267. 22 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.

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