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.
The Algorithmic Foundations of Differential Privacy
This map shows the geographic impact of Aaron Roth'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 Aaron Roth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aaron Roth more than expected).
This network shows the impact of papers produced by Aaron Roth. 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 Aaron Roth. The network helps show where Aaron Roth may publish in the future.
Co-authorship network of co-authors of Aaron Roth
This figure shows the co-authorship network connecting the top 25 collaborators of Aaron Roth.
A scholar is included among the top collaborators of Aaron Roth 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 Aaron Roth. Aaron Roth is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kearns, Michael, et al.. (2020). Convergent Algorithms for (Relaxed) Minimax Fairness.. arXiv (Cornell University).2 indexed citations
5.
Neel, Seth, et al.. (2020). Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. 931–962.4 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.
Neel, Seth, et al.. (2019). Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. arXiv (Cornell University).1 indexed citations
8.
Kearns, Michael, et al.. (2019). Average Individual Fairness: Algorithms, Generalization and Experiments. neural information processing systems. 32. 8240–8249.10 indexed citations
9.
Jung, Christopher, et al.. (2018). Online Learning with an Unknown Fairness Metric. arXiv (Cornell University). 31. 2600–2609.4 indexed citations
10.
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
11.
Kearns, Michael, Aaron Roth, & Zhiwei Steven Wu. (2017). Meritocratic fairness for cross-population selection. International Conference on Machine Learning. 1828–1836.13 indexed citations
12.
Joseph, Matthew, Michael Kearns, Jamie Morgenstern, & Aaron Roth. (2016). Fairness in learning: classic and contextual bandits. Neural Information Processing Systems. 29. 325–333.21 indexed citations
Roth, Aaron, Maria Florina Balcan, Adam Tauman Kalai, & Yishay Mansour. (2010). On the equilibria of alternating move games. Symposium on Discrete Algorithms. 805–816.8 indexed citations
20.
Roth, Aaron & Tim Roughgarden. (2009). The Median Mechanism: Interactive and Efficient Privacy with Multiple Queries. arXiv (Cornell University).10 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.