Byeongchan Kim

Byeongchan Kim is a Ph.D. candidate in the Graduate School of Data Science at Seoul National University, under the supervision of Prof. Min-hwan Oh. He received his M.S. in the Graduate School of Data Science under the supervision of Prof. Min-hwan Oh from Seoul National University, and a B.A. in Mathematics and Statistics from Sungkyunkwan University. His research focuses on offline reinforcement learning (RL) algorithms (e.g., model-free, model-based, goal-conditioned, diffusion, etc), kernel methods, optimization, and their various applications.

Email / Google Scholar / Github

Publications

	Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning Hyungkyu Kang, Byeongchan Kim, Min-hwan Oh (Equal contribution) ICML*, 2026 paper / code
	Peng's Q(λ) for Conservative Value Estimation in Offline Reinforcement Learning Byeongchan Kim, Min-hwan Oh ICLR, 2026 paper / code
	EUGens: Efficient, Unified and General Dense Layers Sang Min Kim, Byeongchan Kim, Arijit Sehanobish, Somnath Basu Roy Chowdhury, Rahul Kidambi, Dongseok Shim, Avinava Dubey, Snigdha Chaturvedi, Min-hwan Oh, Krzysztof Choromanski* (Equal contribution) NeurIPS*, 2025 paper / code
	Model-based Offline Reinforcement Learning with Count-based Conservatism Byeongchan Kim, Min-hwan Oh ICML, 2023 (M.S. Thesis) paper / code

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