Samuel Yeom

1.0k total citations · 1 hit paper
6 papers, 495 citations indexed

About

Samuel Yeom is a scholar working on Artificial Intelligence, Safety Research and Sociology and Political Science. According to data from OpenAlex, Samuel Yeom has authored 6 papers receiving a total of 495 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Safety Research and 1 paper in Sociology and Political Science. Recurrent topics in Samuel Yeom's work include Adversarial Robustness in Machine Learning (4 papers), Ethics and Social Impacts of AI (3 papers) and Privacy-Preserving Technologies in Data (2 papers). Samuel Yeom is often cited by papers focused on Adversarial Robustness in Machine Learning (4 papers), Ethics and Social Impacts of AI (3 papers) and Privacy-Preserving Technologies in Data (2 papers). Samuel Yeom collaborates with scholars based in United States. Samuel Yeom's co-authors include Matt Fredrikson, Somesh Jha, Irene Giacomelli, Michael Carl Tschantz, Zilong Tan, Ameet Talwalkar and Anupam Datta and has published in prestigious journals such as Journal of Computer Security, arXiv (Cornell University) and International Conference on Artificial Intelligence and Statistics.

In The Last Decade

Samuel Yeom

6 papers receiving 474 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

2018 457 citations Samuel Yeom, Irene Giacomelli et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Samuel Yeom United States	4	424	54	48	42	31	6	495
Fatemehsadat Mireshghallah United States	11	285 0.7×	60 1.1×	27 0.6×	28 0.7×	36 1.2×	15	367
Hongsheng Hu Australia	10	355 0.8×	59 1.1×	21 0.4×	35 0.8×	72 2.3×	14	448
Mohammad Al-Rubaie United States	4	226 0.5×	46 0.9×	20 0.4×	33 0.8×	51 1.6×	5	307
Yushun Dong United States	12	349 0.8×	57 1.1×	134 2.8×	28 0.7×	94 3.0×	25	448
Christopher A. Choquette-Choo United States	6	302 0.7×	84 1.6×	12 0.3×	19 0.5×	67 2.2×	10	406
Nuria Rodríguez-Barroso Spain	6	307 0.7×	17 0.3×	14 0.3×	46 1.1×	43 1.4×	10	359
Nazneen Fatema Rajani United States	10	225 0.5×	57 1.1×	46 1.0×	14 0.3×	35 1.1×	29	315
Ziyi Kou United States	11	258 0.6×	60 1.1×	23 0.5×	178 4.2×	96 3.1×	35	392
Dingfan Chen Germany	5	271 0.6×	73 1.4×	10 0.2×	12 0.3×	33 1.1×	9	311
Baiwu Zhang Canada	2	216 0.5×	68 1.3×	9 0.2×	13 0.3×	34 1.1×	2	290

Countries citing papers authored by Samuel Yeom

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Samuel Yeom

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel Yeom. A scholar is included among the top collaborators of Samuel Yeom 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 Samuel Yeom. Samuel Yeom is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Tan, Zilong, Samuel Yeom, Matt Fredrikson, & Ameet Talwalkar. (2020). Learning Fair Representations for Kernel Models.. International Conference on Artificial Intelligence and Statistics. 155–166. 4 indexed citations

2.

Yeom, Samuel & Matt Fredrikson. (2020). Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness. 437–443. 11 indexed citations

3.

Yeom, Samuel, et al.. (2019). Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning. Journal of Computer Security. 28(1). 35–70. 19 indexed citations

4.

Yeom, Samuel & Michael Carl Tschantz. (2018). Discriminative but Not Discriminatory: A Comparison of Fairness Definitions under Different Worldviews.. arXiv (Cornell University). 3 indexed citations

5.

Yeom, Samuel, Anupam Datta, & Matt Fredrikson. (2018). Hunting for Discriminatory Proxies in Linear Regression Models. arXiv (Cornell University). 31. 4568–4578. 1 indexed citations

6.

Yeom, Samuel, Irene Giacomelli, Matt Fredrikson, & Somesh Jha. (2018). Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. 268–282. 457 indexed citations breakdown →

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.

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