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.
Citations per year, relative to Constantine Caramanis Constantine Caramanis (= 1×)
peers
G. George Yin
Countries citing papers authored by Constantine Caramanis
Since
Specialization
Citations
This map shows the geographic impact of Constantine Caramanis'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 Constantine Caramanis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Constantine Caramanis more than expected).
Fields of papers citing papers by Constantine Caramanis
This network shows the impact of papers produced by Constantine Caramanis. 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 Constantine Caramanis. The network helps show where Constantine Caramanis may publish in the future.
Co-authorship network of co-authors of Constantine Caramanis
This figure shows the co-authorship network connecting the top 25 collaborators of Constantine Caramanis.
A scholar is included among the top collaborators of Constantine Caramanis 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 Constantine Caramanis. Constantine Caramanis 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.
Caramanis, Constantine, et al.. (2024). Contextual Pandora’s Box. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 10944–10952.
2.
Caramanis, Constantine, et al.. (2021). Contextual Blocking Bandits. International Conference on Artificial Intelligence and Statistics. 271–279.1 indexed citations
3.
Caramanis, Constantine, et al.. (2020). EM Converges for a Mixture of Many Linear Regressions. International Conference on Artificial Intelligence and Statistics. 1727–1736.1 indexed citations
4.
Caramanis, Constantine, et al.. (2020). The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians. Conference on Learning Theory. 2425–2487.
5.
Qian, Wei, et al.. (2019). Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression. Conference on Learning Theory. 2055–2110.2 indexed citations
6.
Li, Tianyang, et al.. (2019). High Dimensional Robust Estimation of Sparse Models via Trimmed Hard Thresholding.. arXiv (Cornell University).2 indexed citations
7.
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
8.
Yi, Xinyang, Dohyung Park, Yudong Chen, & Constantine Caramanis. (2016). Fast Algorithms for Robust PCA via Gradient Descent. Neural Information Processing Systems. 29. 4152–4160.59 indexed citations
9.
Yi, Xinyang & Constantine Caramanis. (2015). Regularized EM algorithms: a unified framework and statistical guarantees. Neural Information Processing Systems. 28. 1567–1575.7 indexed citations
10.
Papailiopoulos, Dimitris, Ioannis Mitliagkas, Alexandros G. Dimakis, & Constantine Caramanis. (2014). Finding Dense Subgraphs via Low-Rank Bilinear Optimization. International Conference on Machine Learning. 1890–1898.13 indexed citations
Chen, Yudong, Constantine Caramanis, & Shie Mannor. (2013). Robust Sparse Regression under Adversarial Corruption. International Conference on Machine Learning. 774–782.41 indexed citations
13.
Chen, Yudong & Constantine Caramanis. (2013). Noisy and Missing Data Regression: Distribution-Oblivious Support Recovery. International Conference on Machine Learning. 383–391.24 indexed citations
Caramanis, Constantine & Shie Mannor. (2008). Learning in the Limit with Adversarial Disturbances.. Conference on Learning Theory. 467–478.5 indexed citations
Xu, Huan, Shie Mannor, & Constantine Caramanis. (2008). Robustness, Risk, and Regularization in Support Vector Machines. 7. 425–37.2 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.