Han, Seungyub

9 publications

ICML 2025 Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation Taehyun Cho, Seungyub Han, Seokhun Ju, Dohyeong Kim, Kyungjae Lee, Jungwoo Lee

NeurIPS 2025 Pareto Optimal Risk-Agnostic Distributional Bandits with Heavy-Tail Rewards Kyungjae Lee, Dohyeong Kim, Taehyun Cho, Chaeyeon Kim, Yunkyung Ko, Seungyub Han, Seokhun Ju, Dohyeok Lee, Sungbin Lim

ICML 2025 Policy-Labeled Preference Learning: Is Preference Enough for RLHF? Taehyun Cho, Seokhun Ju, Seungyub Han, Dohyeong Kim, Kyungjae Lee, Jungwoo Lee

NeurIPS 2024 Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees Dohyeong Kim, Taehyun Cho, Seungyub Han, Hojun Chung, Kyungjae Lee, Songhwai Oh

UAI 2023 On the Convergence of Continual Learning with Adaptive Methods Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee

NeurIPS 2023 Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion Taehyun Cho, Seungyub Han, Heesoo Lee, Kyungjae Lee, Jungwoo Lee

NeurIPS 2023 SPQR: Controlling Q-Ensemble Independence with Spiked Random Model for Reinforcement Learning Dohyeok Lee, Seungyub Han, Taehyun Cho, Jungwoo Lee

NeurIPSW 2022 Adaptive Methods for Nonconvex Continual Learning Seungyub Han, Yeongmo Kim, Taehyun Cho, Jungwoo Lee

NeurIPSW 2022 Perturbed Quantile Regression for Distributional Reinforcement Learning Taehyun Cho, Seungyub Han, Heesoo Lee, Kyungjae Lee, Jungwoo Lee