| Meta-sage: Scale meta-learning scheduled adaptation with guided exploration for mitigating scale shift on combinatorial optimization J Son*, M Kim*, H Kim, J Park International Conference on Machine Learning, 32194-32210, 2023 | 15* | 2023 |
| RL4CO: a unified reinforcement learning for combinatorial optimization library F Berto*, C Hua*, J Park*, M Kim, H Kim, J Son, H Kim, J Kim, J Park NeurIPS 2023 Workshop: New Frontiers in Graph Learning, 2023 | 13* | 2023 |
| Scale-conditioned adaptation for large scale combinatorial optimization M Kim*, J Son*, H Kim, J Park NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and …, 2022 | 5 | 2022 |
| Solving np-hard min-max routing problems as sequential generation with equity context J Son*, M Kim*, S Choi, H Kim, J Park ICML 2023 Workshop: Sampling and Optimization in Discrete Space, 2023 | 4 | 2023 |
| Equity-Transformer: Solving NP-Hard Min-Max Routing Problems as Sequential Generation with Equity Context J Son*, M Kim*, S Choi, H Kim, J Park Proceedings of the AAAI Conference on Artificial Intelligence 38 (18), 20265 …, 2024 | 3 | 2024 |
| Ant Colony Sampling with GFlowNets for Combinatorial Optimization M Kim*, S Choi*, H Kim, J Son, J Park, Y Bengio arXiv preprint arXiv:2403.07041, 2024 | 2 | 2024 |
| Genetic-guided GFlowNets for Sample Efficient Molecular Optimization H Kim, M Kim, S Choi, J Park arXiv preprint arXiv:2402.05961, 2024 | 2* | 2024 |
| Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization H Kim, M Kim, S Ahn, J Park ICML, 2024 | 1* | 2024 |
| A Neural Separation Algorithm for the Rounded Capacity Inequalities H Kim, J Park, C Kwon INFORMS Journal on Computing, 2024 | 1 | 2024 |
| Neural Coarsening Process for Multi-level Graph Combinatorial Optimization H Kim, M Kim, C Kwon, J Park NeurIPS 2022 Workshop: New Frontiers in Graph Learning, 2022 | 1 | 2022 |
Jinkyoo ParkDepartment of Industrial and Systems Engineering, KAIST在 kaist.ac.kr 的电子邮件经过验证
