Huizhen Yu
- Artificial Intelligence top 5%
- Computational Theory and Mathematics top 5%
- Management Science and Operations Research top 5%
- Control and Systems Engineering top 10%
- Computer Networks and Communications top 10%
- Topics
- Reinforcement Learning in Robotics (13 papers)Markov Chains and Monte Carlo Methods (7 papers)Optimization and Search Problems (6 papers)
- Cited by
- Computational Theory and MathematicsManagement Science and Operations ResearchArtificial Intelligence
- Journals
- IEEE Transactions on Automatic ControlJournal of Mathematical Analysis and ApplicationsJournal of Machine Learning Research
- Partner nations
- United StatesFinlandCanada
In The Last Decade
Huizhen Yu
33 papers receiving 477 citations
Peers
Comparison fields: 5 of 62
- Artificial Intelligence 246
- Computational Theory and Mathematics 150
- Management Science and Operations Research 106
- Control and Systems Engineering 93
- Computer Networks and Communications 88
Countries citing papers authored by Huizhen Yu
This map shows the geographic impact of Huizhen Yu'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 Huizhen Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Huizhen Yu more than expected).
Fields of papers citing papers by Huizhen Yu
This network shows the impact of papers produced by Huizhen Yu. 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 Huizhen Yu. The network helps show where Huizhen Yu may publish in the future.
Co-authorship network of co-authors of Huizhen Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Huizhen Yu. A scholar is included among the top collaborators of Huizhen Yu 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 Huizhen Yu. Huizhen Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 22 | |
| 2 | 4 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 5 | |
| 6 | Weak convergence properties of constrained emphatic temporal-difference learning with constant and slowly diminishing stepsize | 1 |
| 7 | On Convergence of Emphatic Temporal-Difference Learning | 7 |
| 8 | 21 | |
| 9 | 21 | |
| 10 | A Unifying Polyhedral Approximation Framework for Convex Optimization | 1 |
| 11 | Convergence of Least Squares Temporal Difference Methods Under General Conditions | 19 |
| 12 | 17 | |
| 13 | 21 | |
| 14 | Markov Random Fields | 9 |
| 15 | Introduction to Bayesian Networks | 2 |
| 16 | 64 | |
| 17 | 24 | |
| 18 | 42 | |
| 19 | 25 | |
| 20 | An Ecient Method for Large Margin Parameter Optimization in Structured Prediction Problems | 0 |
About Huizhen Yu
Huizhen Yu is a scholar working on Management Science and Operations Research, Statistics and Probability and Artificial Intelligence, having authored 35 papers that have together received 506 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (13 papers), Markov Chains and Monte Carlo Methods (7 papers) and Optimization and Search Problems (6 papers). The work is most often cited by research in Computational Theory and Mathematics (150 citations), Management Science and Operations Research (106 citations) and Artificial Intelligence (246 citations). Huizhen Yu has collaborated with scholars based in United States, Finland and Canada. Frequent co-authors include Dimitri P. Bertsekas, Zhiyong Meng, Yunji Zhang, Chen Zhu, Bo Ren, Pengfei Wei, Hongli Wang, Mu Mu, Xiang‐Yu Huang and Xin Zhang. Their work appears in journals such as IEEE Transactions on Automatic Control, Journal of Mathematical Analysis and Applications and Journal of Machine Learning Research.
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.