Countries citing papers authored by Vitaly Feldman
Since
Specialization
Citations
This map shows the geographic impact of Vitaly Feldman'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 Vitaly Feldman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vitaly Feldman more than expected).
This network shows the impact of papers produced by Vitaly Feldman. 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 Vitaly Feldman. The network helps show where Vitaly Feldman may publish in the future.
Co-authorship network of co-authors of Vitaly Feldman
This figure shows the co-authorship network connecting the top 25 collaborators of Vitaly Feldman.
A scholar is included among the top collaborators of Vitaly Feldman 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 Vitaly Feldman. Vitaly Feldman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Feldman, Vitaly, et al.. (2021). Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry. International Conference on Machine Learning. 393–403.3 indexed citations
2.
Feldman, Vitaly & Kunal Talwar. (2021). Lossless Compression of Efficient Private Local Randomizers. International Conference on Machine Learning. 3208–3219.2 indexed citations
Feldman, Vitaly & Chiyuan Zhang. (2020). What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation. Neural Information Processing Systems. 33. 2881–2891.4 indexed citations
5.
Dagan, Yuval & Vitaly Feldman. (2020). PAC learning with stable and private predictions. Conference on Learning Theory. 1389–1410.
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.