Eunho Yang

2.7k citations
76 papers · 714 · h-index 15

Impact in

Papers in

Eunho Yang

66 papers receiving 689 citations

Peers

Eunho Yang
Comparison fields: 5 of 98
  • Statistics and Probability 136
  • Artificial Intelligence 412
  • Computer Vision and Pattern Recognition 138
  • Computational Mathematics 3
  • Health Informatics 6
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Mladen Kolar United States
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Mikio L. Braun Germany
Guo-Xun Yuan Taiwan
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Citations per field
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Citations per year

Countries citing papers authored by Eunho Yang

Since Specialization
Citations

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).

Fields of papers citing papers by Eunho Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 25 scholars most cited alongside Eunho Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eunho Yang Line = papers co-authored together Eunho Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 76 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Graphical Models via Generalized Linear Models
201262
2
Graphical Models via Univariate Exponential Family Distributions.
201561
3 202144
4
Asymmetric multi-task learning based on task relatedness and loss
201642
5
On NDCG Consistency of Listwise Ranking Methods
201139
6
Mixed graphical models via exponential families
201437
7 202137
8
Federated Continual Learning with Weighted Inter-client Transfer
202129
9
On Poisson Graphical Models
201327
10
Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning
202118
11 202118
12 202217
13
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
201816
14
Dirty Statistical Models
201316
15 201614
16 202314
17 201813
18 201612
19
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
201811
20
Elementary Estimators for High-Dimensional Linear Regression
201411

About Eunho Yang

Eunho Yang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability, Computational Mechanics and Signal Processing, having authored 76 papers that have together received 714 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (19 papers), Statistical Methods and Inference (12 papers), Sparse and Compressive Sensing Techniques (8 papers), Bayesian Modeling and Causal Inference (8 papers), Machine Learning and Data Classification (7 papers), Speech Recognition and Synthesis (7 papers), Advanced Neural Network Applications (6 papers) and Neural Networks and Applications (6 papers). The work is most often cited by research in Statistics and Probability (136 citations), Artificial Intelligence (412 citations), Computer Vision and Pattern Recognition (138 citations), Computational Mathematics (3 citations) and Health Informatics (6 citations). Eunho Yang has collaborated with scholars based in South Korea, United States and Canada. Frequent co-authors include Pradeep Ravikumar, Sung Ju Hwang, Genevera I. Allen, Zhandong Liu, Aurélie Lozano, Pradeep Ravikumar, Ambuj Tewari, Jinwoo Shin, Jaehong Yoon and Seung-Woo Seo. Their work appears in journals such as IEEE Access, Electronic Journal of Statistics, Journal of the Society for Information Display, Wiley Interdisciplinary Reviews Computational Statistics and BMC Systems Biology.

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.

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