This map shows the geographic impact of Rishabh Iyer'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 Rishabh Iyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rishabh Iyer more than expected).
This network shows the impact of papers produced by Rishabh Iyer. 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 Rishabh Iyer. The network helps show where Rishabh Iyer may publish in the future.
Co-authorship network of co-authors of Rishabh Iyer
This figure shows the co-authorship network connecting the top 25 collaborators of Rishabh Iyer.
A scholar is included among the top collaborators of Rishabh Iyer 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 Rishabh Iyer. Rishabh Iyer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Ping, Rishabh Iyer, Ashish V. Tendulkar, Gaurav Aggarwal, & Abir De. (2021). Learning to Select Exogenous Events for Marked Temporal Point Process. Neural Information Processing Systems. 34.1 indexed citations
Iyer, Rishabh, et al.. (2016). Algorithms for optimizing the ratio of submodular functions. International Conference on Machine Learning. 2751–2759.9 indexed citations
Wei, Kai, Rishabh Iyer, & Jeff Bilmes. (2015). Submodularity in Data Subset Selection and Active Learning. International Conference on Machine Learning. 1954–1963.104 indexed citations
13.
Iyer, Rishabh & Jeff Bilmes. (2015). Submodular Point Processes with Applications to Machine Learning. International Conference on Artificial Intelligence and Statistics. 388–397.6 indexed citations
14.
Wei, Kai, et al.. (2015). Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications. arXiv (Cornell University). 28. 2233–2241.10 indexed citations
15.
Wei, Kai, Rishabh Iyer, & Jeff Bilmes. (2014). Fast Multi-stage Submodular Maximization. International Conference on Machine Learning. 1494–1502.27 indexed citations
16.
Iyer, Rishabh, Stefanie Jegelka, & Jeff Bilmes. (2014). Monotone closure of relaxed constraints in submodular optimization: connections between minimization and maximization. Uncertainty in Artificial Intelligence. 360–369.5 indexed citations
17.
Tschiatschek, Sebastian, et al.. (2014). Learning Mixtures of Submodular Functions for Image Collection Summarization. Neural Information Processing Systems. 27. 1413–1421.77 indexed citations
Iyer, Rishabh & Jeff Bilmes. (2012). Submodular-Bregman and the Lovász-Bregman Divergences with Applications. Neural Information Processing Systems. 25. 2933–2941.11 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.