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.
Outcomes of Treatment for Hepatitis C Virus Infection by Primary Care Providers
2011789 citationsSanjeev Arora, Karla Thornton et al.profile →
Proof verification and the hardness of approximation problems
This map shows the geographic impact of Sanjeev Arora'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 Sanjeev Arora with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanjeev Arora more than expected).
This network shows the impact of papers produced by Sanjeev Arora. 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 Sanjeev Arora. The network helps show where Sanjeev Arora may publish in the future.
Co-authorship network of co-authors of Sanjeev Arora
This figure shows the co-authorship network connecting the top 25 collaborators of Sanjeev Arora.
A scholar is included among the top collaborators of Sanjeev Arora 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 Sanjeev Arora. Sanjeev Arora is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Saunshi, Nikunj, et al.. (2021). A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks. arXiv (Cornell University).3 indexed citations
Arora, Sanjeev, Simon S. Du, Wei Hu, et al.. (2019). On Exact Computation with an Infinitely Wide Neural Net. arXiv (Cornell University). 32. 8139–8148.107 indexed citations
10.
Arora, Sanjeev, Andrej Risteski, & Yi Zhang. (2018). Do GANs learn the distribution? Some Theory and Empirics. International Conference on Learning Representations.44 indexed citations
Arora, Sanjeev, et al.. (2017). Project ECHO: an effective means of increasing palliative care capacity.. PubMed. 23(7 Spec No.). SP267–SP271.24 indexed citations
Dubin, Ruth, John Flannery, Paul Taenzer, et al.. (2015). ECHO Ontario Chronic Pain & Opioid Stewardship: Providing Access and Building Capacity for Primary Care Providers in Underserviced, Rural, and Remote Communities.. PubMed. 209. 15–22.36 indexed citations
Arora, Sanjeev, Eli Berger, Elad Hazan, Guy Kindler, & Muli Safra. (2005). On Non-Approximability for Quadratic Programs. Electronic colloquium on computational complexity.16 indexed citations
18.
Allender, Eric, Sanjeev Arora, Michael Kearns, Cristopher Moore, & Alexander Russell. (2002). A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics. Neural Information Processing Systems. 15. 447–454.2 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.