POMO: Policy Optimization with Multiple Optima for Reinforcement Learning. - Yeong-Dae Kwon; Jinho Choo; Byoungjip Kim; Iljoo Yoon; Youngjune Gwon; Seungjai Min (NeurIPS, 2020)

VaB-AL: Incorporating Class Imbalance and Difficulty With Variational Bayes for Active Learning. - Jongwon Choi; Kwang Moo Yi; Jihoon Kim; Jinho Choo; Byoungjip Kim; Jin-Yeop Chang; Youngjune Gwon; Hyung Jin Chang (CVPR, 2021)

Matrix encoding networks for neural combinatorial optimization. - Yeong-Dae Kwon; Jinho Choo; Iljoo Yoon; Minah Park; Duwon Park; Youngjune Gwon (NeurIPS, 2021)

SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning. - Byoungjip Kim; Jinho Choo; Yeong-Dae Kwon; Seongho Joe; Seungjai Min; Youngjune Gwon (CoRR, 2021)

Simulation-guided Beam Search for Neural Combinatorial Optimization. - Jinho Choo; Yeong-Dae Kwon; Jihoon Kim; Jeongwoo Jae; André Hottung; Kevin Tierney; Youngjune Gwon (NeurIPS, 2022)

CAAP: Context-Aware Action Planning Prompting to Solve Computer Tasks with Front-End UI Only. - Junhee Cho; Jihoon Kim; Daseul Bae; Jinho Choo; Youngjune Gwon; Yeong-Dae Kwon (CoRR, 2024)

Reinforcement learning to learn quantum states for Heisenberg scaling accuracy. - Jeongwoo Jae; Jeonghoon Hong; Jinho Choo; Yeong-Dae Kwon (CoRR, 2024)