publications

* Equal contribution.

2026

1. ICML

   Safe In-Context Reinforcement Learning

   Amir Moeini^*, Minjae Kwon^*, Alper Kamil Bozkurt, Yuichi Motai, Rohan Chandra, Lu Feng, and Shangtong Zhang

   International Conference on Machine Learning (ICML), 2026

   Bib PDF

   @inproceedings{moeini2026safeincontext,
     title = {Safe In-Context Reinforcement Learning},
     author = {Moeini, Amir and Kwon, Minjae and Bozkurt, Alper Kamil and Motai, Yuichi and Chandra, Rohan and Feng, Lu and Zhang, Shangtong},
     booktitle = {International Conference on Machine Learning (ICML)},
     year = {2026},
   }

2. ICML

   Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed

   Minjae Kwon, Josephine Lamp, and Lu Feng

   International Conference on Machine Learning (ICML), 2026

   Bib PDF

   @inproceedings{kwon2026safetygeneralization,
     title = {Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed},
     author = {Kwon, Minjae and Lamp, Josephine and Feng, Lu},
     booktitle = {International Conference on Machine Learning (ICML)},
     year = {2026},
   }

3. Preprint

   Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning

   Minjae Kwon^*, Amir Moeini^*, Shangtong Zhang, and Lu Feng

   Preprint, 2026

   arXiv Bib PDF

   @article{kwon2026safeicrllatentbarrier,
     title = {Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning},
     author = {Kwon, Minjae and Moeini, Amir and Zhang, Shangtong and Feng, Lu},
     year = {2026},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
   }

4. Preprint

   Adaptive Shielding for Safe Reinforcement Learning under Hidden-Parameter Dynamics Shifts

   Minjae Kwon, Tyler Ingebrand, Ufuk Topcu, and Lu Feng

   Preprint, 2026

   arXiv Bib PDF

   @article{kwon2026adaptiveshielding,
     title = {Adaptive Shielding for Safe Reinforcement Learning under Hidden-Parameter Dynamics Shifts},
     author = {Kwon, Minjae and Ingebrand, Tyler and Topcu, Ufuk and Feng, Lu},
     year = {2026},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG},
   }

5. Under review

   Algorithm Distillation for Safe In-Context Reinforcement Learning

   Minjae Kwon^*, Amir Moeini^*, Shangtong Zhang, and Lu Feng

   Under review, 2026

   Bib

   @article{kwon2026algorithmdistillation,
     title = {Algorithm Distillation for Safe In-Context Reinforcement Learning},
     author = {Kwon, Minjae and Moeini, Amir and Zhang, Shangtong and Feng, Lu},
     year = {2026}
   }

2025

1. UAI

   Adaptive Reward Design for Reinforcement Learning

   Minjae Kwon, Ingy ElSayed-Aly, and Lu Feng

   Conference on Uncertainty in Artificial Intelligence (UAI), 2025

   Reward design for linear temporal logic specifications

   Bib PDF

   @inproceedings{kwon2025adaptiverewarddesign,
     title = {Adaptive Reward Design for Reinforcement Learning},
     author = {Kwon, Minjae and ElSayed-Aly, Ingy and Feng, Lu},
     booktitle = {Conference on Uncertainty in Artificial Intelligence (UAI)},
     year = {2025},
     note = {Reward design for linear temporal logic specifications},
   }

2023

1. ICLR

   Recursive Time Series Data Augmentation

   Amine Mohamed Aboussalah, Minjae Kwon, Raj G Patel, Cheng Chi, and Chi-Guhn Lee

   International Conference on Learning Representations (ICLR), 2023

   Bib PDF

   @inproceedings{aboussalah2023recursive,
     title = {Recursive Time Series Data Augmentation},
     author = {Aboussalah, Amine Mohamed and Kwon, Minjae and Patel, Raj G and Chi, Cheng and Lee, Chi-Guhn},
     booktitle = {International Conference on Learning Representations (ICLR)},
     year = {2023},
   }

2020

1. Mathematics

   On Nonnil-S-Noetherian Rings

   Minjae Kwon and Jung Wook Lim

   Mathematics, 2020

   Bib PDF

   @article{kwon2020nonnil,
     title = {On Nonnil-{S}-Noetherian Rings},
     author = {Kwon, Minjae and Lim, Jung Wook},
     journal = {Mathematics},
     volume = {8},
     number = {9},
     pages = {1428},
     year = {2020},
   }