This map shows the geographic impact of Eunho Yang'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 Eunho Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eunho Yang more than expected).
This network shows the impact of papers produced by Eunho Yang. 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 Eunho Yang. The network helps show where Eunho Yang may publish in the future.
Co-authorship network of co-authors of Eunho Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Eunho Yang.
A scholar is included among the top collaborators of Eunho Yang 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 Eunho Yang. Eunho Yang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lozano, Aurélie, et al.. (2021). Adaptive Proximal Gradient Methods for Structured Neural Networks. Neural Information Processing Systems. 34.2 indexed citations
6.
Yang, Eunho, et al.. (2021). Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning. Neural Information Processing Systems. 34.17 indexed citations
7.
Yoon, Jaehong, et al.. (2020). Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning. arXiv (Cornell University).9 indexed citations
8.
Zheng, Peng, et al.. (2019). Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning.. International Conference on Machine Learning. 7242–7251.1 indexed citations
9.
Hwang, Sung Ju, et al.. (2019). Sparsity Normalization: Stabilizing the Expected Outputs of Deep Networks.. arXiv (Cornell University).1 indexed citations
10.
Hwang, Sung Ju, et al.. (2018). Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding. Neural Information Processing Systems. 31. 1368–1378.15 indexed citations
11.
Liu, Yanbin, Minseop Park, Saehoon Kim, et al.. (2018). Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning. UTS ePRESS (University of Technology Sydney).11 indexed citations
12.
Yang, Eunho, et al.. (2016). Asymmetric Multi-task Learning based on Task Relatedness and Confidence.. International Conference on Machine Learning. 230–238.5 indexed citations
13.
Yang, Eunho, et al.. (2016). Asymmetric multi-task learning based on task relatedness and loss. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 230–238.42 indexed citations
14.
Yang, Eunho, Aurélie Lozano, & Pradeep Ravikumar. (2015). Closed-form estimators for high-dimensional generalized linear models. Neural Information Processing Systems. 28. 586–594.2 indexed citations
Yang, Eunho, Aurélie Lozano, & Pradeep Ravikumar. (2014). Elementary Estimators for High-Dimensional Linear Regression. International Conference on Machine Learning. 388–396.11 indexed citations
17.
Yang, Eunho, Aurélie Lozano, & Pradeep Ravikumar. (2014). Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. International Conference on Machine Learning. 397–405.10 indexed citations
18.
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
19.
Yang, Eunho, Pradeep Ravikumar, Genevera I. Allen, & Zhandong Liu. (2013). On Poisson Graphical Models. Neural Information Processing Systems. 26. 1718–1726.26 indexed citations
20.
Ravikumar, Pradeep, Ambuj Tewari, & Eunho Yang. (2011). On NDCG Consistency of Listwise Ranking Methods. International Conference on Artificial Intelligence and Statistics. 15. 618–626.39 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.