| Regularizing class-wise predictions via self-knowledge distillation S Yun, J Park, K Lee, J Shin Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 285 | 2020 |
| Surf: Semi-supervised reward learning with data augmentation for feedback-efficient preference-based reinforcement learning J Park, Y Seo, J Shin, H Lee, P Abbeel, K Lee arXiv preprint arXiv:2203.10050, 2022 | 60 | 2022 |
| Preference transformer: Modeling human preferences using transformers for rl C Kim, J Park, J Shin, H Lee, P Abbeel, K Lee arXiv preprint arXiv:2303.00957, 2023 | 31 | 2023 |
| Opencos: Contrastive semi-supervised learning for handling open-set unlabeled data J Park, S Yun, J Jeong, J Shin European Conference on Computer Vision, 134-149, 2022 | 29 | 2022 |
| Object-aware regularization for addressing causal confusion in imitation learning J Park, Y Seo, C Liu, L Zhao, T Qin, J Shin, TY Liu Advances in Neural Information Processing Systems 34, 3029-3042, 2021 | 17 | 2021 |
| Meta-learning with self-improving momentum target J Tack, J Park, H Lee, J Lee, J Shin Advances in Neural Information Processing Systems 35, 6318-6332, 2022 | 6 | 2022 |
| SuRe: Improving Open-domain Question Answering of LLMs via Summarized Retrieval J Kim, J Nam, S Mo, J Park, SW Lee, M Seo, JW Ha, J Shin The Twelfth International Conference on Learning Representations, 2023 | 2 | 2023 |
| SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs J Kim, J Nam, S Mo, J Park, SW Lee, M Seo, JW Ha, J Shin arXiv preprint arXiv:2404.13081, 2024 | | 2024 |
| Regularizing Predictions via Class-wise Self-knowledge Distillation S Yun, J Park, K Lee, J Shin | | 2019 |
