Xingrui Yu

2.4k total citations · 1 hit paper
17 papers, 1.0k citations indexed

About

Xingrui Yu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Civil and Structural Engineering. According to data from OpenAlex, Xingrui Yu has authored 17 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Civil and Structural Engineering. Recurrent topics in Xingrui Yu's work include Machine Learning and Data Classification (4 papers), Machine Learning and Algorithms (2 papers) and Human Pose and Action Recognition (2 papers). Xingrui Yu is often cited by papers focused on Machine Learning and Data Classification (4 papers), Machine Learning and Algorithms (2 papers) and Human Pose and Action Recognition (2 papers). Xingrui Yu collaborates with scholars based in China, Australia and United Kingdom. Xingrui Yu's co-authors include Ivor W. Tsang, Masashi Sugiyama, Bo Han, Miao Xu, Gang Niu, Quanming Yao, Weihua Hu, Peng Ren, Chunbo Luo and Xiaomin Wu and has published in prestigious journals such as Journal of Hazardous Materials, Journal of Colloid and Interface Science and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Xingrui Yu

15 papers receiving 1.0k citations

Hit Papers

Co-teaching: Robust Training of Deep Neural Networks with... 2018 2026 2020 2023 2018 200 400 600
What are hit papers?

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.

  1. Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
    2018 664 citations Bo Han, Quanming Yao et al. Neural Information Processing Systems profile →

Peers

Xingrui Yu
Hongwei Zhao China
Xiaoya Li China
Ignazio Gallo Italy
Wenkai Zhang China
Liguo Weng China
Cheng Tan China
Md. Monirul Islam Bangladesh
Hongwei Zhao China
Xingrui Yu
Citations per year, relative to Xingrui Yu Xingrui Yu (= 1×) peers Hongwei Zhao

Countries citing papers authored by Xingrui Yu

Since Specialization
Citations

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

Fields of papers citing papers by Xingrui Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xingrui Yu

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

All Works

17 of 17 papers shown
1.
2.
Wan, Zhenhua, et al.. (2025). POI Recommendation via Multi-Objective Adversarial Imitation Learning. Proceedings of the AAAI Conference on Artificial Intelligence. 39(12). 12676–12684.
3.
Yu, Xingrui, Bo Han, & Ivor W. Tsang. (2024). USN: A Robust Imitation Learning Method against Diverse Action Noise. Journal of Artificial Intelligence Research. 79. 1237–1280. 1 indexed citations
4.
Yu, Xingrui, et al.. (2024). Construction of a highly stable natural silicate-supported molybdenum catalyst for efficient epoxidation of olefins. Journal of Colloid and Interface Science. 660. 490–501. 7 indexed citations
5.
Xiong, Chao, et al.. (2022). Tuning the olefin-VOCs epoxidation performance of ceria by mechanochemical loading of coinage metal. Journal of Hazardous Materials. 441. 129888–129888. 14 indexed citations
6.
Yu, Xingrui, et al.. (2020). Intrinsic Reward Driven Imitation Learning via Generative Model. arXiv (Cornell University). 1. 10925–10935. 5 indexed citations
7.
Yu, Xingrui, Bo Han, Jiangchao Yao, et al.. (2019). How Does Disagreement Benefit Co-teaching?. arXiv (Cornell University). 13 indexed citations
8.
Yu, Xingrui, et al.. (2019). Design and Experiment of Pneumatic Conveying Seeder with no-tillage for Simultaneous Seeding of Wheat Seed and Fertilizer. 2019 Boston, Massachusetts July 7- July 10, 2019. 1 indexed citations
9.
Yu, Xingrui, He Zhang, Chunbo Luo, Hairong Qi, & Peng Ren. (2018). Oil Spill Segmentation via Adversarial $f$ -Divergence Learning. IEEE Transactions on Geoscience and Remote Sensing. 56(9). 4973–4988. 41 indexed citations
10.
Han, Bo, Gang Niu, Jiangchao Yao, et al.. (2018). Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels. arXiv (Cornell University). 8 indexed citations
11.
Han, Bo, Quanming Yao, Xingrui Yu, et al.. (2018). Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels. Neural Information Processing Systems. 31. 8527–8537. 664 indexed citations breakdown →
12.
Yu, Xingrui, et al.. (2018). Stereo Matching via Dual Fusion. IEEE Signal Processing Letters. 25(5). 615–619. 3 indexed citations
13.
Han, Bo, Gang Niu, Xingrui Yu, et al.. (2018). SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. arXiv (Cornell University). 1. 4006–4016. 23 indexed citations
14.
Li, Hui, Xingrui Yu, & Peng Ren. (2018). Typhoon Cloud Prediction Via Generative Adversarial Networks. 28. 3023–3026. 1 indexed citations
15.
Yu, Leijian, Cai Luo, Xingrui Yu, et al.. (2018). Deep learning for vision-based micro aerial vehicle autonomous landing. International Journal of Micro Air Vehicles. 10(2). 171–185. 19 indexed citations
16.
Yu, Xingrui, Xiaomin Wu, Chunbo Luo, & Peng Ren. (2017). Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GIScience & Remote Sensing. 54(5). 741–758. 228 indexed citations
17.
Yu, Xingrui, et al.. (2017). Level sets with self-guided filtering for marine oil spill segmentation. 1772–1775. 6 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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