Jiangchao Yao

2.4k total citations
64 papers, 1.3k citations indexed

About

Jiangchao Yao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Organic Chemistry. According to data from OpenAlex, Jiangchao Yao has authored 64 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Artificial Intelligence, 19 papers in Computer Vision and Pattern Recognition and 12 papers in Organic Chemistry. Recurrent topics in Jiangchao Yao's work include Domain Adaptation and Few-Shot Learning (10 papers), Machine Learning and Data Classification (8 papers) and Topic Modeling (7 papers). Jiangchao Yao is often cited by papers focused on Domain Adaptation and Few-Shot Learning (10 papers), Machine Learning and Data Classification (8 papers) and Topic Modeling (7 papers). Jiangchao Yao collaborates with scholars based in China, United States and Hong Kong. Jiangchao Yao's co-authors include Dawei Ma, Yongda Zhang, Fenggang Tao, Shi‐Hui Wu, Ya Zhang, Ivor W. Tsang, Hongxia Yang, Jingren Zhou, Alan R. Katritzky and Jun Sun and has published in prestigious journals such as Journal of the American Chemical Society, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Medicinal Chemistry.

In The Last Decade

Jiangchao Yao

51 papers receiving 1.3k citations

Peers

Jiangchao Yao
Lijun Zhang China
N. V. Subba Reddy India
Zhi‐Hua Zhou China
Francesc Serratosa Spain
Aleksandar Ilić Serbia
Siming Wang China
Sangwon Seo United States
Niranjan Balasubramanian United States
Lijun Zhang China
Jiangchao Yao
Citations per year, relative to Jiangchao Yao Jiangchao Yao (= 1×) peers Lijun Zhang

Countries citing papers authored by Jiangchao Yao

Since Specialization
Citations

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

Fields of papers citing papers by Jiangchao Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiangchao Yao

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

All Works

20 of 20 papers shown
1.
Cao, Xiaofeng, Xin Yu, Jiangchao Yao, et al.. (2025). Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation. ACM Computing Surveys. 58(6). 1–47.
2.
Liu, Yikun, Yajie Zhang, Xiaolong Jiang, et al.. (2025). LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant. 4015–4025.
3.
Chen, Wanyi, et al.. (2025). Multi-modal Medical Diagnosis via Large-small Model Collaboration. 30763–30773.
4.
Yao, Jiangchao, et al.. (2025). Trustworthy Machine Learning: From Data to Models. 7(2-3). 74–246. 1 indexed citations
5.
Li, Hangyu, Yixin Zhang, Jiangchao Yao, Nannan Wang, & Bo Han. (2025). Towards Regularized Mixture of Predictions for Class-Imbalanced Semi-Supervised Facial Expression Recognition. 1377–1385.
6.
Yao, Jiangchao, et al.. (2025). Redundancy-Adaptive Multimodal Learning for imperfect data. Neural Networks. 191. 107821–107821.
7.
Wu, Chaoyi, Xiaoman Zhang, Yao Zhang, et al.. (2025). UniBrain: Universal Brain MRI diagnosis with hierarchical knowledge-enhanced pre-training. Computerized Medical Imaging and Graphics. 122. 102516–102516. 3 indexed citations
8.
Zhang, Ruipeng, et al.. (2024). Fairness-guided federated training for generalization and personalization in cross-silo federated learning. Frontiers of Information Technology & Electronic Engineering. 26(1). 42–61.
9.
Yao, Jiangchao, et al.. (2024). Balanced Destruction-Reconstruction Dynamics for Memory-Replay Class Incremental Learning. IEEE Transactions on Image Processing. 33. 4966–4981. 2 indexed citations
10.
Yao, Jiangchao, et al.. (2024). Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning. 27371–27380. 1 indexed citations
11.
Wang, Jinyi, Ben Fei, Huangjie Zheng, et al.. (2024). MVTexGen: Synthesising 3D Textures Using Multi-View Diffusion. 1–6.
12.
Yang, Yuhuan, Chen Ju, Fei Zhang, et al.. (2024). Multi-modal Prototypes for Open-World Semantic Segmentation. International Journal of Computer Vision. 132(12). 6004–6020. 1 indexed citations
13.
Zhang, Shengyu, Jiangchao Yao, Fuli Feng, et al.. (2023). Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems. IEEE Transactions on Knowledge and Data Engineering. 36(2). 459–474. 8 indexed citations
14.
Yao, Jiangchao, et al.. (2023). Latent Class-Conditional Noise Model. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(8). 9964–9980. 3 indexed citations
15.
Wang, Yanfeng, et al.. (2022). FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation. 131–140. 8 indexed citations
16.
Zheng, Huangjie, Chen Xu, Jiangchao Yao, et al.. (2021). Contrastive Conditional Transport for Representation Learning.. arXiv (Cornell University). 1 indexed citations
17.
Yu, Xingrui, Bo Han, Jiangchao Yao, et al.. (2019). How Does Disagreement Benefit Co-teaching?. arXiv (Cornell University). 13 indexed citations
18.
Yao, Jiangchao, Hao Wu, Ya Zhang, Ivor W. Tsang, & Jun Sun. (2019). Safeguarded Dynamic Label Regression for Noisy Supervision. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 9103–9110. 27 indexed citations
19.
Han, Bo, Jiangchao Yao, Gang Niu, et al.. (2018). Masking: A New Perspective of Noisy Supervision. UTS ePRESS (University of Technology Sydney). 31. 5836–5846. 33 indexed citations
20.
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

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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