Zhaohan Daniel Guo
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Control and Systems Engineering
- Automotive Engineering
- Co-authors
- Bilal PiotPablo SprechmannSteven KapturowskiCharles BlundellAdrià Puigdomènech BadiaEmma BrunskillPhilip S. ThomasJean-Bastien Grill
- Topics
- Reinforcement Learning in Robotics (3 papers)Evolutionary Algorithms and Applications (1 paper)Auction Theory and Applications (1 paper)
- Journals
- arXiv (Cornell University)International Conference on Machine LearningNational Conference on Artificial Intelligence
- Partner nations
- United StatesUnited KingdomChina
In The Last Decade
Zhaohan Daniel Guo
5 papers receiving 88 citations
Peers
Comparison fields: 5 of 31
- Artificial Intelligence 75
- Computer Vision and Pattern Recognition 14
- Computational Theory and Mathematics 13
- Control and Systems Engineering 12
- Automotive Engineering 7
Countries citing papers authored by Zhaohan Daniel Guo
This map shows the geographic impact of Zhaohan Daniel Guo'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 Zhaohan Daniel Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhaohan Daniel Guo more than expected).
Fields of papers citing papers by Zhaohan Daniel Guo
This network shows the impact of papers produced by Zhaohan Daniel Guo. 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 Zhaohan Daniel Guo. The network helps show where Zhaohan Daniel Guo may publish in the future.
Co-authorship network of co-authors of Zhaohan Daniel Guo
This figure shows the co-authorship network connecting the top 25 collaborators of Zhaohan Daniel Guo. A scholar is included among the top collaborators of Zhaohan Daniel Guo 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 Zhaohan Daniel Guo. Zhaohan Daniel Guo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Agent57: Outperforming the Atari Human Benchmark | 71 |
| 2 | Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning | 4 |
| 3 | 8 | |
| 4 | 3 | |
| 5 | Concurrent PAC RL | 5 |
About Zhaohan Daniel Guo
Zhaohan Daniel Guo is a scholar working on Management Science and Operations Research, Artificial Intelligence and Economics and Econometrics, having authored 5 papers that have together received 91 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Evolutionary Algorithms and Applications (1 paper) and Auction Theory and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (75 citations), Computational Theory and Mathematics (13 citations) and Computer Science Applications (4 citations). Zhaohan Daniel Guo has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Bilal Piot, Pablo Sprechmann, Steven Kapturowski, Charles Blundell, Adrià Puigdomènech Badia, Emma Brunskill, Philip S. Thomas, Jean-Bastien Grill, Rémi Munos and Bernardo Ávila Pires. Their work appears in journals such as arXiv (Cornell University), International Conference on Machine Learning and National Conference on Artificial Intelligence.
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.