Jiyan Yang
Impact in
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- Stochastic Gradient Optimization Techniques
Papers in
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- Stochastic Gradient Optimization Techniques 8
- Advanced Graph Neural Networks 2
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- Sparse and Compressive Sensing Techniques 6
- Co-authors
- Michael W. Mahoney (10 shared papers)Yinlam Chow (7 shared papers)Ram Rajagopal (3 shared papers)Junjie Qin (4 shared papers)Ting Qiu (1 shared paper)Haixing Li (1 shared paper)Zhibing Huang (1 shared paper)Yusheng Cao (1 shared paper)
- Journals
- BMC Pregnancy and Childbirth (1 paper)Journal of Chromatography A (1 paper)IEEE Transactions on Smart Grid (1 paper)Journal of Machine Learning Research (1 paper)Analytical Chemistry (1 paper)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Jiyan Yang
21 papers receiving 298 citations
Peers
Comparison fields: 5 of 67
- Computational Mathematics 5
- Artificial Intelligence 96
- Computer Vision and Pattern Recognition 49
- Computer Networks and Communications 46
- Control and Systems Engineering 43
Countries citing papers authored by Jiyan Yang
This map shows the geographic impact of Jiyan Yang'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 Jiyan Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiyan Yang more than expected).
Fields of papers citing papers by Jiyan Yang
This network shows the impact of papers produced by Jiyan Yang. 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 Jiyan Yang. The network helps show where Jiyan Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Jiyan Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 43 | |
| 2 | 2009 | 40 | |
| 3 | 2021 | 28 | |
| 4 | 2016 | 26 | |
| 5 | 2015 | 24 | |
| 6 | 2015 | 23 | |
| 7 | 2014 | 23 | |
| 8 | 2015 | 20 | |
| 9 | 2023 | 15 | |
| 10 | 2014 | 12 | |
| 11 | 2016 | 9 | |
| 12 | 2023 | 8 | |
| 13 | Sub-sampled Newton Methods with Non-uniform Sampling | 2016 | 8 |
| 14 | Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery | 2021 | 6 |
| 15 | Feature-distributed sparse regression: a screen-and-clean approach | 2016 | 5 |
| 16 | 2021 | 5 | |
| 17 | 2015 | 3 | |
| 18 | 2016 | 3 | |
| 19 | 2024 | 2 | |
| 20 | 2025 | 2 |
About Jiyan Yang
Jiyan Yang is a scholar working on Artificial Intelligence, Computational Mechanics, Information Systems, Electrical and Electronic Engineering and Statistics and Probability, having authored 25 papers that have together received 306 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (8 papers), Sparse and Compressive Sensing Techniques (6 papers), Recommender Systems and Techniques (4 papers), Smart Grid Energy Management (4 papers), Optimal Power Flow Distribution (3 papers), Microgrid Control and Optimization (2 papers), Markov Chains and Monte Carlo Methods (2 papers) and Advanced Graph Neural Networks (2 papers). The work is most often cited by research in Computational Mathematics (5 citations), Artificial Intelligence (96 citations), Computer Vision and Pattern Recognition (49 citations), Computer Networks and Communications (46 citations) and Control and Systems Engineering (43 citations). Jiyan Yang has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Michael W. Mahoney, Yinlam Chow, Ram Rajagopal, Junjie Qin, Ting Qiu, Haixing Li, Zhibing Huang, Yusheng Cao, Xiangrui Meng and James Zou. Their work appears in journals such as BMC Pregnancy and Childbirth, Journal of Chromatography A, IEEE Transactions on Smart Grid, Journal of Machine Learning Research and Analytical Chemistry.
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.