Kaixiang Lin

1.8k total citations · 1 hit paper
21 papers, 667 citations indexed

About

Kaixiang Lin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Kaixiang Lin has authored 21 papers receiving a total of 667 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 3 papers in Computer Networks and Communications. Recurrent topics in Kaixiang Lin's work include Domain Adaptation and Few-Shot Learning (5 papers), Sparse and Compressive Sensing Techniques (3 papers) and Multimodal Machine Learning Applications (3 papers). Kaixiang Lin is often cited by papers focused on Domain Adaptation and Few-Shot Learning (5 papers), Sparse and Compressive Sensing Techniques (3 papers) and Multimodal Machine Learning Applications (3 papers). Kaixiang Lin collaborates with scholars based in United States, China and Canada. Kaixiang Lin's co-authors include Jiayu Zhou, Anil K. Jain, Zhuangdi Zhu, İnci M. Baytaş, Anna Tarakanova, Mohammad Madani, Liyang Xie, Zhaojian Li, Tianshu Chu and Dong Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Molecular Sciences and IEEE Transactions on Power Systems.

In The Last Decade

Kaixiang Lin

21 papers receiving 647 citations

Hit Papers

Transfer Learning in Deep Reinforcement Learning: A Survey 2023 2026 2024 2025 2023 100 200 300
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. Transfer Learning in Deep Reinforcement Learning: A Survey
    2023 349 citations Zhuangdi Zhu, Kaixiang Lin et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kaixiang Lin United States 10 277 165 120 108 73 21 667
Guoqiang Li China 14 438 1.6× 163 1.0× 176 1.5× 84 0.8× 48 0.7× 27 702
Peter Vrancx Belgium 12 328 1.2× 278 1.7× 105 0.9× 68 0.6× 117 1.6× 50 768
Longhua Ma China 16 201 0.7× 200 1.2× 152 1.3× 97 0.9× 84 1.2× 68 698
Maximilian Ernestus Germany 3 282 1.0× 161 1.0× 213 1.8× 115 1.1× 113 1.5× 3 755
Mehdi Ramezani Iran 14 226 0.8× 247 1.5× 153 1.3× 95 0.9× 46 0.6× 42 821
Jaffar Atwan Jordan 5 405 1.5× 106 0.6× 89 0.7× 104 1.0× 48 0.7× 11 664
Jianji Ren China 13 297 1.1× 224 1.4× 86 0.7× 72 0.7× 206 2.8× 33 739
Mohamad Khairi Ishak Malaysia 13 136 0.5× 214 1.3× 146 1.2× 72 0.7× 148 2.0× 119 693
I. Grondman Netherlands 6 349 1.3× 203 1.2× 269 2.2× 74 0.7× 170 2.3× 8 841
Junfu Chen China 12 254 0.9× 94 0.6× 46 0.4× 53 0.5× 66 0.9× 29 476

Countries citing papers authored by Kaixiang Lin

Since Specialization
Citations

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

Fields of papers citing papers by Kaixiang Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaixiang Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Kaixiang Lin. A scholar is included among the top collaborators of Kaixiang Lin 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 Kaixiang Lin. Kaixiang Lin 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.
Li, Li Erran, et al.. (2024). Socratic Human Feedback (SoHF): Expert Steering Strategies for LLM Code Generation. 15491–15502. 1 indexed citations
2.
Chen, J., et al.. (2024). An Energy-Efficient Edge Processor for Radar-Based Continuous Fall Detection Utilizing Mixed-Radix FFT and Updated Blockwise Computation. IEEE Internet of Things Journal. 11(19). 32117–32128. 2 indexed citations
3.
Mansimov, Elman, et al.. (2024). Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk. 9500–9522. 3 indexed citations
4.
Lin, Kaixiang, et al.. (2023). A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg. Journal of Artificial Intelligence Research. 78. 1143–1200. 3 indexed citations
5.
Zhu, Zhuangdi, Kaixiang Lin, Anil K. Jain, & Jiayu Zhou. (2023). Transfer Learning in Deep Reinforcement Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(11). 13344–13362. 349 indexed citations breakdown →
6.
Shi, Xingjian, et al.. (2023). Automated Few-Shot Classification with Instruction-Finetuned Language Models. 2414–2432. 1 indexed citations
7.
Gao, Qiaozi, et al.. (2022). DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following. IEEE Robotics and Automation Letters. 7(4). 10049–10056. 23 indexed citations
8.
Zhu, Zhuangdi, Kaixiang Lin, Bo Dai, & Jiayu Zhou. (2022). Self-Adaptive Imitation Learning: Learning Tasks with Delayed Rewards from Sub-optimal Demonstrations. Proceedings of the AAAI Conference on Artificial Intelligence. 36(8). 9269–9277. 8 indexed citations
9.
Madani, Mohammad, Kaixiang Lin, & Anna Tarakanova. (2021). DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks. International Journal of Molecular Sciences. 22(24). 13555–13555. 38 indexed citations
10.
Zhu, Zhuangdi, Kaixiang Lin, Bo Dai, & Jiayu Zhou. (2021). Off-Policy Imitation Learning from Observations. arXiv (Cornell University). 33. 12402–12413. 2 indexed citations
11.
Liu, Boyang, Ding Wang, Kaixiang Lin, Pang‐Ning Tan, & Jiayu Zhou. (2021). RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection. PubMed. 2021. 1505–1511. 25 indexed citations
12.
Chen, Dong, Zhaojian Li, Tianshu Chu, et al.. (2021). PowerNet: Multi-Agent Deep Reinforcement Learning for Scalable Powergrid Control. IEEE Transactions on Power Systems. 37(2). 1007–1017. 54 indexed citations
13.
Kwabi, David G., Kaixiang Lin, Yunlong Ji, et al.. (2018). Alkaline Quinone Flow Battery with Long Lifetime at pH 12. Joule. 2(9). 1907–1908. 43 indexed citations
14.
Xie, Liyang, İnci M. Baytaş, Kaixiang Lin, & Jiayu Zhou. (2017). Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates. 1195–1204. 40 indexed citations
15.
Baytaş, İnci M., Kaixiang Lin, Fei Wang, Anil K. Jain, & Jiayu Zhou. (2016). Stochastic convex sparse principal component analysis. PubMed. 2016(1). 15–15. 5 indexed citations
16.
Lin, Kaixiang, et al.. (2016). Multi-Task Feature Interaction Learning. 1735–1744. 24 indexed citations
17.
Baytaş, İnci M., Kaixiang Lin, Fei Wang, Anil K. Jain, & Jiayu Zhou. (2016). PhenoTree: Interactive Visual Analytics for Hierarchical Phenotyping From Large-Scale Electronic Health Records. IEEE Transactions on Multimedia. 18(11). 2257–2270. 14 indexed citations
18.
Lin, Kaixiang & Jiayu Zhou. (2016). Interactive Multi-task Relationship Learning. 52. 241–250. 9 indexed citations
19.
Lin, Kaixiang, et al.. (2016). Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine. 108–116. 9 indexed citations
20.
Lin, Kaixiang & Dong Liu. (2014). Category-based dynamic recommendations adaptive to user interest drifts. 1–6. 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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