Ziyu Wang
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Electrical and Electronic Engineering
- Control and Systems Engineering top 5%
- Computer Networks and Communications top 5%
- Co-authors
- Nando de FreitasTom SchaulMarc LanctotHado van HasseltMatteo HesselPablo Samuel CastroYuanyuan LiJun Gong
- Topics
- Reinforcement Learning in Robotics (6 papers)Adversarial Robustness in Machine Learning (5 papers)Robot Manipulation and Learning (3 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Ziyu Wang
15 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Artificial Intelligence 742
- Computer Vision and Pattern Recognition 372
- Electrical and Electronic Engineering 245
- Control and Systems Engineering 244
- Computer Networks and Communications 229
Countries citing papers authored by Ziyu Wang
This map shows the geographic impact of Ziyu 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 Ziyu Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ziyu Wang more than expected).
Fields of papers citing papers by Ziyu Wang
This network shows the impact of papers produced by Ziyu 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 Ziyu Wang. The network helps show where Ziyu Wang may publish in the future.
Co-authorship network of co-authors of Ziyu Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Ziyu Wang. A scholar is included among the top collaborators of Ziyu Wang 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 Ziyu Wang. Ziyu Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 10 | |
| 5 | 4 | |
| 6 | 5 | |
| 7 | 3 | |
| 8 | 127 | |
| 9 | Critic Regularized Regression | 1 |
| 10 | 164 | |
| 11 | 3 | |
| 12 | 2 | |
| 13 | A Framework for Data-Driven Robotics | 4 |
| 14 | Learning an Embedding Space for Transferable Robot Skills | 66 |
| 15 | 23 | |
| 16 | Robust imitation of diverse behaviors | 25 |
| 17 | Dueling Network Architectures for Deep Reinforcement Learningbreakdown → | 1034 |
| 18 | 2 |
About Ziyu Wang
Ziyu Wang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering, having authored 18 papers that have together received 1.5k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (6 papers), Adversarial Robustness in Machine Learning (5 papers) and Robot Manipulation and Learning (3 papers). The work is most often cited by research in Artificial Intelligence (742 citations), Computer Vision and Pattern Recognition (372 citations) and Automotive Engineering (162 citations). Ziyu Wang has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Nando de Freitas, Tom Schaul, Marc Lanctot, Hado van Hasselt, Matteo Hessel, Pablo Samuel Castro, Yuanyuan Li, Jun Gong, Yü Liu and Zhiqin Zhu. Their work appears in journals such as Nature, IEEE Access and Pattern Recognition Letters.
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.