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Heeyoung Kim

As seen in: Wiley Online Library, arXiv, MDPI, Taylor & Francis Online, ACS Publications, BioRxiv, Cell Reports, AIP Publishing, WARC
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Articles

Cluster-Based Classification for Mixed-Type Data Under Data Scarcity

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By Jeonghyun Choi, Heeyoung Kim, Woojin Doo
| Wiley Online Library Verified
1 Introduction Data scarcity represents a fundamental challenge in machine learning applications where training data are limited due to cost constraints, privacy concerns, or the inherent rarity of certain phenomena [1-3]. This challenge is particularly acute when dealing with heterogeneous populations that exhibit diverse characteristics, leading to a classical trade-off between model specificity and data sufficiency.
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Computer Science > Machine Learning

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By Junsub Jung, Heeyoung Kim
| arXiv
Causality has become an increasingly important tool for gaining a deeper understanding of complex systems. Among various causal analysis methods, causal discovery, which identifies causal relationships among variables from data, has been widely used to uncover underlying causality in diverse processes.
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Computer Science > Artificial Intelligence

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By Heeyoung Kim, Sanha Chang
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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