Jiyan Yang
- Artificial Intelligence top 10%
- Stochastic Gradient Optimization Techniques 8
- Data Stream Mining Techniques 2
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- Caching and Content Delivery 2
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- Statistical Methods and Inference 2
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- Sparse and Compressive Sensing Techniques 6
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- Smart Grid Energy Management 4
- Optimal Power Flow Distribution 3
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- Recommender Systems and Techniques 4
Jiyan Yang
21 papers receiving 285 citations
Peers
Comparison fields: 5 of 68
- Computational Mathematics 3
- Artificial Intelligence 94
- Computer Vision and Pattern Recognition 46
- Computer Networks and Communications 44
- Statistics and Probability 15
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2023 | 7 | |
| 4 | 2023 | 0 | |
| 5 | 2023 | 14 | |
| 6 | Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery | 2021 | 6 |
| 7 | 2021 | 26 | |
| 8 | Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning | 2018 | 1 |
| 9 | Sub-sampled Newton Methods with Non-uniform Sampling | 2016 | 7 |
| 10 | Feature-distributed sparse regression: a screen-and-clean approach | 2016 | 5 |
| 11 | 2016 | 0 | |
| 12 | 2016 | 3 | |
| 13 | 2016 | 8 | |
| 14 | 2015 | 3 | |
| 15 | 2015 | 43 | |
| 16 | 2015 | 19 | |
| 17 | 2015 | 23 | |
| 18 | 2014 | 11 | |
| 19 | 2014 | 21 | |
| 20 | 2009 | 40 |
About Jiyan Yang
Jiyan Yang is a scholar working on Artificial Intelligence, Statistics and Probability, Computational Mechanics, Information Systems and Endocrine and Autonomic Systems, having authored 24 papers that have together received 292 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (8 papers), Sparse and Compressive Sensing Techniques (6 papers), Smart Grid Energy Management (4 papers), Recommender Systems and Techniques (4 papers), Optimal Power Flow Distribution (3 papers), Caching and Content Delivery (2 papers), Statistical Methods and Inference (2 papers) and Data Stream Mining Techniques (2 papers). The work is most often cited by research in Computational Mathematics (3 citations), Artificial Intelligence (94 citations), Computer Vision and Pattern Recognition (46 citations), Computer Networks and Communications (44 citations) and Statistics and Probability (15 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, Yusheng Cao, Haixing Li, Zhibing Huang, Ting Qiu, Xiangrui Meng and Prabhat. Their work appears in journals such as IEEE Transactions on Power Systems, Analytical Chemistry, BMC Pregnancy and Childbirth, Proceedings of the IEEE 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.