Joonseong Kang — accepted AI-conference papers

2026

ORBIT: Optimal Recommendation Framework for Boarding with Interpretable Timelines

Joonseong Kang, Jaehun Bang, Seung Ha Hwang, Jiyoung Ko, Subeen Park, Jeffrey Gennari

IJCAI 2026

Determining when to leave for the airport is a complex problem shaped by flight delay risk, traffic, weather, airport congestion, and passenger-specific constraints. Existing services rely on isolated delay estimates or simple travel time calculations, failing to capture real-time context. We propos

Cited by 0 Scholar ↗

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…

Cited by 0 Source ↗code ↗Scholar ↗