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.
Countries citing papers authored by Pradeep Ravikumar
Since
Specialization
Citations
This map shows the geographic impact of Pradeep Ravikumar'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 Pradeep Ravikumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pradeep Ravikumar more than expected).
Fields of papers citing papers by Pradeep Ravikumar
This network shows the impact of papers produced by Pradeep Ravikumar. 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 Pradeep Ravikumar. The network helps show where Pradeep Ravikumar may publish in the future.
Co-authorship network of co-authors of Pradeep Ravikumar
This figure shows the co-authorship network connecting the top 25 collaborators of Pradeep Ravikumar.
A scholar is included among the top collaborators of Pradeep Ravikumar 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 Pradeep Ravikumar. Pradeep Ravikumar 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.
Rosenfeld, Elan, Pradeep Ravikumar, & Andrej Risteski. (2021). The Risks of Invariant Risk Minimization. arXiv (Cornell University).5 indexed citations
2.
Balakrishnan, Sivaraman, et al.. (2020). A Robust Univariate Mean Estimator is All You Need.. International Conference on Artificial Intelligence and Statistics. 4034–4044.1 indexed citations
3.
Ravikumar, Pradeep, et al.. (2019). On Human-Aligned Risk Minimization. Neural Information Processing Systems. 32. 15055–15064.5 indexed citations
4.
Nagarajan, Vaishnavh, et al.. (2019). Revisiting Adversarial Risk. International Conference on Artificial Intelligence and Statistics. 2331–2339.1 indexed citations
Ravikumar, Pradeep, et al.. (2018). Connecting Optimization and Regularization Paths. Neural Information Processing Systems. 31. 10608–10619.6 indexed citations
7.
Yen, Ian En-Hsu, Satyen Kale, Felix X. Yu, et al.. (2018). Loss Decomposition for Fast Learning in Large Output Spaces.. International Conference on Machine Learning. 5626–5635.3 indexed citations
8.
Li, Tianyang, Xinyang Yi, Constantine Caramanis, & Pradeep Ravikumar. (2017). Minimax Gaussian Classification & Clustering. International Conference on Artificial Intelligence and Statistics. 1–9.2 indexed citations
9.
Yen, Ian En-Hsu, Xiangru Huang, Kai Zhong, Pradeep Ravikumar, & Inderjit S. Dhillon. (2016). PD-sparse: a primal and dual sparse approach to extreme multiclass and multilabel classification. International Conference on Machine Learning. 3069–3077.65 indexed citations
10.
Natarajan, Nagarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, & Inderjit S. Dhillon. (2016). Optimal classification with multivariate losses. International Conference on Machine Learning. 1530–1538.2 indexed citations
11.
Yen, Ian En-Hsu, Kai Zhong, Cho‐Jui Hsieh, Pradeep Ravikumar, & Inderjit S. Dhillon. (2015). Sparse Linear Programming via primal and dual augmented coordinate descent. Neural Information Processing Systems. 28. 2368–2376.10 indexed citations
12.
Ravikumar, Pradeep, et al.. (2014). A Representation Theory for Ranking Functions. Neural Information Processing Systems. 27. 361–369.7 indexed citations
13.
Wang, Huahua, Arindam Banerjee, Cho‐Jui Hsieh, Pradeep Ravikumar, & Inderjit S. Dhillon. (2013). Large Scale Distributed Sparse Precision Estimation. Neural Information Processing Systems. 26. 584–592.17 indexed citations
14.
Yang, Eunho, Ambuj Tewari, & Pradeep Ravikumar. (2013). On robust estimation of high dimensional generalized linear models. International Joint Conference on Artificial Intelligence. 1834–1840.3 indexed citations
15.
Yang, Eunho, Pradeep Ravikumar, Genevera I. Allen, & Zhandong Liu. (2013). On Poisson Graphical Models. Neural Information Processing Systems. 26. 1718–1726.26 indexed citations
Agarwal, Alekh, Martin J. Wainwright, Peter L. Bartlett, & Pradeep Ravikumar. (2009). Information-theoretic lower bounds on the oracle complexity of convex optimization. arXiv (Cornell University). 22. 1–9.32 indexed citations
18.
Vu, Vincent Q., Bin Yu, Thomas Naselaris, et al.. (2008). Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images. Neural Information Processing Systems. 21. 1337–1344.3 indexed citations
19.
Liu, Han, Larry Wasserman, John Lafferty, & Pradeep Ravikumar. (2007). SpAM: Sparse Additive Models. Neural Information Processing Systems. 20. 1201–1208.89 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.