This map shows the geographic impact of Shay Moran'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 Shay Moran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shay Moran more than expected).
This network shows the impact of papers produced by Shay Moran. 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 Shay Moran. The network helps show where Shay Moran may publish in the future.
Co-authorship network of co-authors of Shay Moran
This figure shows the co-authorship network connecting the top 25 collaborators of Shay Moran.
A scholar is included among the top collaborators of Shay Moran 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 Shay Moran. Shay Moran is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dziugaite, Gintare Karolina, et al.. (2021). Towards a Unified Information-Theoretic Framework for Generalization. arXiv (Cornell University). 34.1 indexed citations
3.
Hazan, Elad, et al.. (2021). Multiclass Boosting and the Cost of Weak Learning. Neural Information Processing Systems. 34.2 indexed citations
4.
Livni, Roi & Shay Moran. (2020). A Limitation of the PAC-Bayes Framework. Neural Information Processing Systems. 33. 20543–20553.1 indexed citations
5.
Bassily, Raef, et al.. (2020). Learning from Mixtures of Private and Public Populations. arXiv (Cornell University). 33. 2947–2957.
Balsubramani, Akshay, Sanjoy Dasgupta, Yoav Freund, & Shay Moran. (2019). An adaptive nearest neighbor rule for classification. eScholarship (California Digital Library). 32. 7577–7586.1 indexed citations
10.
Bousquet, Olivier, Daniel M. Kane, & Shay Moran. (2019). The Optimal Approximation Factor in Density Estimation.. eScholarship (California Digital Library). 318–341.
11.
Bousquet, Olivier, Roi Livni, & Shay Moran. (2019). Passing Tests without Memorizing: Two Models for Fooling Discriminators.. arXiv (Cornell University).1 indexed citations
Kane, Daniel M., Roi Livni, Shay Moran, & Amir Yehudayoff. (2017). On Communication Complexity of Classification Problems. eScholarship (California Digital Library). 24. 177–1943.1 indexed citations
15.
Bringmann, Karl, László Kozma, Shay Moran, & N. S. Narayanaswamy. (2016). Hitting Set for Hypergraphs of Low VC-dimension. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics).2 indexed citations
16.
Moran, Shay, et al.. (2016). Supervised learning through the lens of compression. Neural Information Processing Systems. 29. 2784–2792.5 indexed citations
17.
Moran, Shay & Amir Yehudayoff. (2015). Proper PAC learning is compressing. arXiv (Cornell University). 22. 40.
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.