This map shows the geographic impact of Aarti Singh'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 Aarti Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aarti Singh more than expected).
This network shows the impact of papers produced by Aarti Singh. 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 Aarti Singh. The network helps show where Aarti Singh may publish in the future.
Co-authorship network of co-authors of Aarti Singh
This figure shows the co-authorship network connecting the top 25 collaborators of Aarti Singh.
A scholar is included among the top collaborators of Aarti Singh 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 Aarti Singh. Aarti Singh 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.
Xu, Yichong, Sivaraman Balakrishnan, Aarti Singh, & Artur Dubrawski. (2020). Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. Journal of Machine Learning Research. 21(162). 1–54.
Balakrishnan, Sivaraman, Min Xu, Akshay Krishnamurthy, & Aarti Singh. (2018). Noise Thresholds for Spectral Clustering. Figshare. 24. 954–962.12 indexed citations
8.
Wang, Yining, Adams Wei Yu, & Aarti Singh. (2017). On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models. arXiv (Cornell University). 18(143). 1–41.4 indexed citations
9.
Balakrishnan, Sivaraman, Simon S. Du, Jerry Li, & Aarti Singh. (2017). Computationally Efficient Robust Sparse Estimation in High Dimensions. Conference on Learning Theory. 169–212.10 indexed citations
10.
Allen-Zhu, Zeyuan, Yuanzhi Li, Aarti Singh, & Yining Wang. (2017). Near-Optimal Design of Experiments via Regret Minimization. International Conference on Machine Learning. 126–135.12 indexed citations
11.
Wang, Yining & Aarti Singh. (2017). Provably correct algorithms for matrix column subset selection with selectively sampled data. Journal of Machine Learning Research. 18(1). 5699–5740.10 indexed citations
12.
Wang, Yining, Simon S. Du, Sivaraman Balakrishnan, & Aarti Singh. (2017). Stochastic Zeroth-order Optimization in High Dimensions.. International Conference on Artificial Intelligence and Statistics. 1356–1365.11 indexed citations
Reddi, Sashank J., Aaditya Ramdas, Barnabás Póczos, Aarti Singh, & Larry Wasserman. (2015). On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. International Conference on Artificial Intelligence and Statistics. 772–780.6 indexed citations
Reddi, Sashank J., Aaditya Ramdas, Barnabás Póczos, Aarti Singh, & Larry Wasserman. (2014). Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions.. arXiv (Cornell University).4 indexed citations
18.
Singh, Aarti, et al.. (2013). A Study on Energy Efficient Routing Protocolsin MANETs with Effect on Selfish Behaviour. International Journal of Innovative Research in Computer and Communication Engineering. 1(7). 1386–1400.1 indexed citations
19.
Fasy, Brittany Terese, Fabrizio Lecci, Alessandro Rinaldo, et al.. (2013). Statistical Inference For Persistent Homology: Confidence Sets For Persistence Diagrams. arXiv (Cornell University).3 indexed citations
20.
Balakrishnan, Sivaraman, Brittany Terese Fasy, Fabrizio Lecci, et al.. (2013). Statistical Inference For Persistent Homology. arXiv (Cornell University).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.