Gyeongdeok Seo — accepted AI-conference papers

2026

Dissecting Causal Mechanism Shifts via FANS: Function And Noise Separation

Gyeongdeok Seo, Jaeyoon Shim, Mingyu Kim, Hoyoon Byun, Yonghan Jung, Kyungwoo Song

ICML 2026poster

Identifying the drivers of causal mechanism shifts, distinguishing functional changes from noise alterations, known as dissection, is a critical yet under-explored problem in data science (e.g., biomedical science and manufacturing). This paper introduces a more general and unified framework, the fu…

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2025

CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization

Hoyoon Byun, Gyeongdeok Seo, Joonseong Kang, Taero Kim, Jihee Kim, Kyungwoo Song

NeurIPS 2025poster

In-context learning (ICL), a nonparametric learning method based on the knowledge of demonstration sets, has become a de facto standard for large language models (LLMs). The primary goal of ICL is to select valuable demonstration sets to enhance the performance of LLMs. Traditional ICL methods choos…

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