Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion. - Taehyun Cho; Seungyub Han; Heesoo Lee; Kyungjae Lee 001; Jungwoo Lee 001 (NeurIPS, 2023)

SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning. - Dohyeok Lee; Seungyub Han; Taehyun Cho; Jungwoo Lee 001 (NeurIPS, 2023)

On the Convergence of Continual Learning with Adaptive Methods. - Seungyub Han; Yeongmo Kim; Taehyun Cho; Jungwoo Lee 001 (UAI, 2023)

Learning to Learn Unlearned Feature for Brain Tumor Segmentation. - Seungyub Han; Yeongmo Kim; Seokhyeon Ha; Jungwoo Lee 001; Seunghong Choi (CoRR, 2023)

Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees. - Dohyeong Kim; Taehyun Cho; Seungyub Han; Hojun Chung; Kyungjae Lee 001; Songhwai Oh (NeurIPS, 2024)

D2NAS: Efficient Neural Architecture Search With Performance Improvement and Model Size Reduction for Diverse Tasks. - Jungeun Lee; Seungyub Han; Jungwoo Lee 001 (IEEE Access, 2024)

Tractable and Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation. - Taehyun Cho; Seungyub Han; Kyungjae Lee 001; Seokhun Ju; Dohyeong Kim; Jungwoo Lee 001 (CoRR, 2024)

Deterministic Uncertainty Estimation for Multi-Modal Regression With Deep Neural Networks. - Jaehak Cho; Jae Myung Kim; Seungyub Han; Jungwoo Lee 001 (IEEE Access, 2025)

Policy-labeled Preference Learning: Is Preference Enough for RLHF? - Taehyun Cho; Seokhun Ju; Seungyub Han; Dohyeong Kim; Kyungjae Lee 001; Jungwoo Lee 001 (CoRR, 2025)