An Efficient Neural Network Architecture for Rate Maximization in Energy Harvesting Downlink Channels. - Heasung Kim; Taehyun Cho; Jungwoo Lee 001; Wonjae Shin; H. Vincent Poor (ISIT, 2020)

Chebyshev Polynomial Codes: Task Entanglement-based Coding for Distributed Matrix Multiplication. - Sangwoo Hong; Heecheol Yang; Youngseok Yoon; Taehyun Cho; Jungwoo Lee 001 (ICML, 2021)

Optimized Shallow Neural Networks for Sum-Rate Maximization in Energy Harvesting Downlink Multiuser NOMA Systems. - Heasung Kim; Taehyun Cho; Jungwoo Lee 001; Wonjae Shin; H. Vincent Poor (IEEE J. Sel. Areas Commun., 2021)

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)

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

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)