Seth Neel

1.1k total citations
12 papers, 119 citations indexed

About

Seth Neel is a scholar working on Artificial Intelligence, Safety Research and Management Science and Operations Research. According to data from OpenAlex, Seth Neel has authored 12 papers receiving a total of 119 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Safety Research and 2 papers in Management Science and Operations Research. Recurrent topics in Seth Neel's work include Privacy-Preserving Technologies in Data (4 papers), Ethics and Social Impacts of AI (4 papers) and Adversarial Robustness in Machine Learning (3 papers). Seth Neel is often cited by papers focused on Privacy-Preserving Technologies in Data (4 papers), Ethics and Social Impacts of AI (4 papers) and Adversarial Robustness in Machine Learning (3 papers). Seth Neel collaborates with scholars based in United States, Israel and United Kingdom. Seth Neel's co-authors include Aaron Roth, Michael Kearns, Zhiwei Steven Wu, Christopher Jung, Katrina Ligett, Shahin Jabbari, Bo Waggoner, Logan Stapleton, Steven Y. Wu and Matthew Joseph and has published in prestigious journals such as SHILAP Revista de lepidopterología, arXiv (Cornell University) and International Conference on Machine Learning.

In The Last Decade

Seth Neel

12 papers receiving 115 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seth Neel United States	7	92	45	19	14	13	12	119
Pallavi Baljekar India	7	97 1.1×	51 1.1×	15 0.8×	19 1.4×	3 0.2×	10	160
Vijay Keswani United States	3	86 0.9×	73 1.6×	8 0.4×	11 0.8×	8 0.6×	10	125
Preethi Lahoti Finland	6	68 0.7×	34 0.8×	5 0.3×	13 0.9×	6 0.5×	7	98
Kamrun Naher Keya United States	6	119 1.3×	92 2.0×	30 1.6×	27 1.9×	12 0.9×	10	200
David Madras Canada	4	88 1.0×	52 1.2×	5 0.3×	11 0.8×	7 0.5×	9	133
Elliot Creager Canada	6	131 1.4×	40 0.9×	6 0.3×	7 0.5×	5 0.4×	11	166
Vasileios Iosifidis Germany	5	134 1.5×	62 1.4×	6 0.3×	11 0.8×	9 0.7×	7	176
Hansa Srinivasan United States	2	39 0.4×	52 1.2×	15 0.8×	21 1.5×	3 0.2×	3	92
John Aslanides United Kingdom	4	142 1.5×	27 0.6×	11 0.6×	9 0.6×	2 0.2×	5	182
Arjun Roy Germany	5	104 1.1×	66 1.5×	5 0.3×	16 1.1×	10 0.8×	10	151

Countries citing papers authored by Seth Neel

Since Specialization

Citations

This map shows the geographic impact of Seth Neel'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 Seth Neel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seth Neel more than expected).

Fields of papers citing papers by Seth Neel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seth Neel. 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 Seth Neel. The network helps show where Seth Neel may publish in the future.

Co-authorship network of co-authors of Seth Neel

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

All Works

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12 of 12 papers shown

1.

Wang, Jason, et al.. (2023). MoPe: Model Perturbation based Privacy Attacks on Language Models. 13647–13660. 6 indexed citations

2.

Jung, Christopher, Michael Kearns, Seth Neel, et al.. (2021). An Algorithmic Framework for Fairness Elicitation.. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 19. 2 indexed citations

3.

Jung, Christopher, et al.. (2021). A new analysis of differential privacy’s generalization guarantees (invited paper). 9–9. 4 indexed citations

4.

Neel, Seth, et al.. (2020). Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. 931–962. 4 indexed citations

5.

Neel, Seth, et al.. (2019). Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. arXiv (Cornell University). 1 indexed citations

6.

Jung, Christopher, Michael Kearns, Seth Neel, et al.. (2019). Eliciting and Enforcing Subjective Individual Fairness.. arXiv (Cornell University). 13 indexed citations

7.

Jabbari, Shahin, et al.. (2019). Fair Algorithms for Learning in Allocation Problems. 170–179. 28 indexed citations

8.

Wu, Steven Y., Aaron Roth, Katrina Ligett, Bo Waggoner, & Seth Neel. (2019). Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. SHILAP Revista de lepidopterología. 9(2). 8 indexed citations

9.

Neel, Seth, et al.. (2019). Oracle Efficient Private Non-Convex Optimization. arXiv (Cornell University). 1. 7243–7252. 2 indexed citations

10.

Joseph, Matthew, Michael Kearns, Jamie Morgenstern, Seth Neel, & Aaron Roth. (2018). Meritocratic Fairness for Infinite and Contextual Bandits. 158–163. 12 indexed citations

11.

Kearns, Michael, Seth Neel, Aaron Roth, & Zhiwei Steven Wu. (2017). Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. International Conference on Machine Learning. 2564–2572. 25 indexed citations

12.

Ligett, Katrina, Seth Neel, Aaron Roth, Bo Waggoner, & Zhiwei Steven Wu. (2017). Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. arXiv (Cornell University). 30. 2566–2576. 14 indexed citations

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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