Samuel Yeom
Impact in
- Health Informatics top 5%
- Artificial Intelligence top 5%
- Privacy-Preserving Technologies in Data
- Adversarial Robustness in Machine Learning
- Cryptography and Data Security
- Stochastic Gradient Optimization Techniques
- Explainable Artificial Intelligence (XAI)
Papers in
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- Adversarial Robustness in Machine Learning 4
- Privacy-Preserving Technologies in Data 2
- Explainable Artificial Intelligence (XAI) 2
- Gaussian Processes and Bayesian Inference 1
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- Ethics and Social Impacts of AI 3
- Co-authors
- Matt Fredrikson (5 shared papers)Somesh Jha (2 shared papers)Irene Giacomelli (2 shared papers)Michael Carl Tschantz (1 shared paper)Zilong Tan (1 shared paper)Ameet Talwalkar (1 shared paper)Anupam Datta (1 shared paper)
- Journals
- Journal of Computer Security (1 paper)arXiv (Cornell University) (2 papers)International Conference on Artificial Intelligence and Statistics (1 paper)
- Partner nations
- United States
In The Last Decade
Samuel Yeom
6 papers receiving 474 citations
Samuel Yeom's Hit Papers
Peers
Comparison fields: 5 of 78
- Health Informatics 24
- Artificial Intelligence 424
- Safety Research 48
- Computer Vision and Pattern Recognition 54
- Computer Science Applications 14
Countries citing papers authored by Samuel Yeom
This map shows the geographic impact of Samuel Yeom'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 Samuel Yeom with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel Yeom more than expected).
Fields of papers citing papers by Samuel Yeom
This network shows the impact of papers produced by Samuel Yeom. 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 Samuel Yeom. The network helps show where Samuel Yeom may publish in the future.
Co-authors
The 7 scholars most cited alongside Samuel Yeom, 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 | Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting Hit paper breakdown → | 2018 | 457 |
| 2 | 2019 | 19 | |
| 3 | 2020 | 11 | |
| 4 | Learning Fair Representations for Kernel Models. | 2020 | 4 |
| 5 | Discriminative but Not Discriminatory: A Comparison of Fairness Definitions under Different Worldviews. | 2018 | 3 |
| 6 | 2018 | 1 |
About Samuel Yeom
Samuel Yeom is a scholar working on Artificial Intelligence, Safety Research, Sociology and Political Science, Gender Studies and Infectious Diseases, having authored 6 papers that have together received 495 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (4 papers), Ethics and Social Impacts of AI (3 papers), Privacy-Preserving Technologies in Data (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Crime Patterns and Interventions (1 paper), Corruption and Economic Development (1 paper), Qualitative Comparative Analysis Research (1 paper) and Gaussian Processes and Bayesian Inference (1 paper). The work is most often cited by research in Health Informatics (24 citations), Artificial Intelligence (424 citations), Safety Research (48 citations), Computer Vision and Pattern Recognition (54 citations) and Computer Science Applications (14 citations). Samuel Yeom has collaborated with scholars based in United States. Frequent co-authors include Matt Fredrikson, Somesh Jha, Irene Giacomelli, Michael Carl Tschantz, Zilong Tan, Ameet Talwalkar and Anupam Datta. Their work appears in journals such as Journal of Computer Security, arXiv (Cornell University) and International Conference on Artificial Intelligence and Statistics.
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.