Lequn Wang
Impact in
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- Video Surveillance and Tracking Methods
- Human Pose and Action Recognition
- Face recognition and analysis
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
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- Gait Recognition and Analysis
Papers in
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- Advanced Bandit Algorithms Research 4
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- Machine Learning and Algorithms 2
- Stochastic Gradient Optimization Techniques 2
- Domain Adaptation and Few-Shot Learning 1
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Xu Zou (1 shared paper)Bharath Hariharan (1 shared paper)Yurong You (1 shared paper)Vincent Chen (1 shared paper)Gao Huang (1 shared paper)Kilian Q. Weinberger (1 shared paper)Yan Wang (1 shared paper)Thorsten Joachims (5 shared papers)
- Journals
- AI Magazine (1 paper)Fire Control and Command Control (1 paper)International Conference on Machine Learning (2 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Lequn Wang
7 papers receiving 225 citations
Peers
Comparison fields: 5 of 33
- Computer Vision and Pattern Recognition 190
- Biomedical Engineering 84
- Management Science and Operations Research 21
- Artificial Intelligence 38
- Safety, Risk, Reliability and Quality 9
Countries citing papers authored by Lequn Wang
This map shows the geographic impact of Lequn Wang'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 Lequn Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lequn Wang more than expected).
Fields of papers citing papers by Lequn Wang
This network shows the impact of papers produced by Lequn Wang. 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 Lequn Wang. The network helps show where Lequn Wang may publish in the future.
Co-authors
The 19 scholars most cited alongside Lequn Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 191 | |
| 2 | 2023 | 13 | |
| 3 | CAB: Continuous Adaptive Blending for Policy Evaluation and Learning | 2019 | 12 |
| 4 | 2021 | 8 | |
| 5 | Fairness of Exposure in Stochastic Bandits | 2021 | 3 |
| 6 | 2024 | 1 | |
| 7 | Optimizing and Sequencing of the Important Defended Targets in Air-defense Operation based on Interval-number | 2010 | 1 |
| 8 | Cost-Sensitive Learning via Deep Policy ERM | 2018 | 1 |
About Lequn Wang
Lequn Wang is a scholar working on Management Science and Operations Research, Artificial Intelligence, Information Systems, Electrical and Electronic Engineering and Computer Networks and Communications, having authored 8 papers that have together received 230 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (4 papers), Machine Learning and Algorithms (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Adversarial Robustness in Machine Learning (1 paper), Age of Information Optimization (1 paper), Statistical Methods in Clinical Trials (1 paper) and Recommender Systems and Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (190 citations), Biomedical Engineering (84 citations), Management Science and Operations Research (21 citations), Artificial Intelligence (38 citations) and Safety, Risk, Reliability and Quality (9 citations). Lequn Wang has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Xu Zou, Bharath Hariharan, Yurong You, Vincent Chen, Gao Huang, Kilian Q. Weinberger, Yan Wang, Thorsten Joachims, Yi Su and Michele Santacatterina. Their work appears in journals such as AI Magazine, Fire Control and Command Control and International Conference on Machine Learning.
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.