Author

Eui-Jin Kim

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Preprint Aug 2026

Neural-Bayesian Structure Learning for Discrete Choice Modeling

Conventional discrete choice and machine learning models are estimated primarily from observational data and typically treat explanatory covariates as parallel inputs, providing no internal mechanism for determining how related attributes should adjust when one is deliberately changed. This paper proposes Neural-Bayesi...

Hyun-Ah Yun, Eun Hak Lee, Jia-Ru Zhang et al. · 0 citations

Preprint Aug 2026

Knowledge-Data-Dual-Driven Reinforcement Learning for Autonomous Vehicle Control in Mixed Traffic

This work proposes Knowledge-Data Dual-driven Reinforcement Learning (KDDRL), a conditional deep generative model that effectively handles intention uncertainty, accelerates training convergence, and outperforms conventional baseline methods in terms of safety, efficiency, and comfort.

Jie Fang, Wei Zheng, Mengyun Xu et al. · 0 citations