Cyril Zhang
- Artificial Intelligence
- Management Science and Operations Research
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Computational Mechanics
- Co-authors
- Elad HazanKaran SinghYi ZhangSanjeev AroraNaman AgarwalBrian BullinsDanielle BraggXinyi Chen
- Topics
- Sparse and Compressive Sensing Techniques (3 papers)Markov Chains and Monte Carlo Methods (2 papers)Stochastic Gradient Optimization Techniques (2 papers)
- Journals
- arXiv (Cornell University)International Conference on Machine LearningInternational Conference on Learning Representations
- Partner nations
- United States
In The Last Decade
Cyril Zhang
5 papers receiving 13 citations
Peers
Comparison fields: 5 of 15
- Artificial Intelligence 10
- Management Science and Operations Research 6
- Computer Vision and Pattern Recognition 5
- Control and Systems Engineering 3
- Computational Mechanics 3
Countries citing papers authored by Cyril Zhang
This map shows the geographic impact of Cyril Zhang'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 Cyril Zhang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cyril Zhang more than expected).
Fields of papers citing papers by Cyril Zhang
This network shows the impact of papers produced by Cyril Zhang. 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 Cyril Zhang. The network helps show where Cyril Zhang may publish in the future.
Co-authorship network of co-authors of Cyril Zhang
This figure shows the co-authorship network connecting the top 25 collaborators of Cyril Zhang. A scholar is included among the top collaborators of Cyril Zhang 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 Cyril Zhang. Cyril Zhang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Revisiting the Generalization of Adaptive Gradient Methods | 2 |
| 3 | Towards Provable Control for Unknown Linear Dynamical Systems | 4 |
| 4 | The Case for Full-Matrix Adaptive Regularization | 0 |
| 5 | 2 | |
| 6 | Efficient Regret Minimization in Non-Convex Games | 6 |
About Cyril Zhang
Cyril Zhang is a scholar working on Statistics and Probability, Numerical Analysis and Management Science and Operations Research, having authored 6 papers that have together received 16 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (3 papers), Markov Chains and Monte Carlo Methods (2 papers) and Stochastic Gradient Optimization Techniques (2 papers). The work is most often cited by research in Management Science and Operations Research (6 citations), Numerical Analysis (2 citations) and Human-Computer Interaction (2 citations). Cyril Zhang has collaborated with scholars based in United States. Frequent co-authors include Elad Hazan, Karan Singh, Karan Singh, Yi Zhang, Sanjeev Arora, Naman Agarwal, Brian Bullins, Danielle Bragg, Yi Zhang and Xinyi Chen. Their work appears in journals such as arXiv (Cornell University), International Conference on Machine Learning and International Conference on Learning Representations.
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.