Guang Cheng
- Computational Mathematics top 2%
- Statistics and Probability top 0.5%
- Statistical Methods and Inference 39
- Statistical Methods and Bayesian Inference 21
- Advanced Statistical Methods and Models 14
- Advanced Causal Inference Techniques 4
- Soil Science top 5%
- Artificial Intelligence top 5%
- Bayesian Methods and Mixture Models 9
- Machine Learning and Data Classification 5
- Finance top 10%
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- Sparse and Compressive Sensing Techniques 6
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- Distributed Sensor Networks and Detection Algorithms 6
- Co-authors
- Jianhua Z. HuangTeri C. BalserChao LiangDevin L. WixonXianyang ZhangZuofeng ShangHao Helen ZhangYufeng Liu
- Journals
- Journal of the American Statistical Association (9 papers)PLoS ONE (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (2 papers)
- Partner nations
- United StatesChinaCanada
In The Last Decade
Guang Cheng
66 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 131
- Computational Mathematics 74
- Statistics and Probability 657
- Soil Science 183
- Artificial Intelligence 326
- Finance 56
Countries citing papers authored by Guang Cheng
This map shows the geographic impact of Guang Cheng'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 Guang Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guang Cheng more than expected).
Fields of papers citing papers by Guang Cheng
This network shows the impact of papers produced by Guang Cheng. 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 Guang Cheng. The network helps show where Guang Cheng may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Guang Cheng, 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 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 1 | |
| 7 | On the Algorithmic Stability of Adversarial Training | 2021 | 9 |
| 8 | Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network | 2020 | 6 |
| 9 | Non-asymptotic Analysis for Nonparametric Testing | 2020 | 2 |
| 10 | Directional Pruning of Deep Neural Networks | 2020 | 5 |
| 11 | Bootstrapping Upper Confidence Bound | 2019 | 1 |
| 12 | Sharp Theoretical Analysis for Nonparametric Testing under Random Projection | 2019 | 1 |
| 13 | Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data | 2016 | 11 |
| 14 | 2016 | 83 | |
| 15 | 2016 | 27 | |
| 16 | 2013 | 6 | |
| 17 | 2012 | 9 | |
| 18 | 2012 | 41 | |
| 19 | 2011 | 116 | |
| 20 | Super Point Detection Based on Sampling and Data Streaming Algorithms | 2009 | 0 |
About Guang Cheng
Guang Cheng is a scholar working on Statistics and Probability, Computational Mathematics and Artificial Intelligence, having authored 76 papers that have together received 1.3k indexed citations. Recurring topics across this work include Statistical Methods and Inference (39 papers), Statistical Methods and Bayesian Inference (21 papers), Advanced Statistical Methods and Models (14 papers), Bayesian Methods and Mixture Models (9 papers), Sparse and Compressive Sensing Techniques (6 papers), Distributed Sensor Networks and Detection Algorithms (6 papers), Machine Learning and Data Classification (5 papers) and Advanced Causal Inference Techniques (4 papers). The work is most often cited by research in Computational Mathematics (74 citations), Statistics and Probability (657 citations) and Soil Science (183 citations). Guang Cheng has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Jianhua Z. Huang, Teri C. Balser, Chao Liang, Devin L. Wixon, Xianyang Zhang, Zuofeng Shang, Hao Helen Zhang, Yufeng Liu, Stanislav Volgushev and Han Liu. Their work appears in journals such as Journal of the American Statistical Association, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine 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.