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Hosan Jeong

dblp:211/7598 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Security and privacy · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Deep learning architectures and training · 50% Trustworthy machine learning · 50%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training
data augmentation		 0.512021
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity · ICLR 2021
Machine learning › Trustworthy machine learning
interpretability		 0.512021
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity · ICLR 2021
Machine learning › Deep learning architectures and training › data augmentation
mixup		 0.512021
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity · ICLR 2021
Machine learning › Trustworthy machine learning › interpretability › visual explanation
saliency		 0.512021
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity · ICLR 2021

Methods — techniques the papers use, named apart from their topics

supermodular diversity · 0.5saliency guidance · 0.5
YearPublicationVenuePosition
2023 Efficient Liquidity Providing via Margin Liquidity
abstract
The liquidity of the exchange is the most important factor when crytocurrency traders choose an exchange. However, the amount of liquidity provided by the liquidity providers in decentralized exchanges is insufficient when compared to centralized exchanges. This is because the liquidity providers in decentralized exchanges suffer from the risk of divergence loss inherent to the automated market making system. To this end, we introduce a new concept called margin liquidity and leverage this concept to propose a highly profitable margin liquidity-providing position. Then, we extend this margin liquidity-providing position to a virtual margin liquidity-providing position to alleviate the risk of divergence loss for the liquidity providers and encourage them to provide more liquidity to the pool. We show that our proposed margin liquidity is 8K times more capital efficient than the concentrated liquidity proposed in Uniswap V3.
Yeonwoo Jeong, Chanyoung Jeoung, Hosan Jeong, Sangyoon Han, Juntae Kim
 ICBC3
2021 Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Wonho Choo, Hosan Jeong, Hyun Oh Song
 ICLR3