| Continual learning with node-importance based adaptive group sparse regularization S Jung, H Ahn, S Cha, T Moon Advances in neural information processing systems 33, 3647-3658, 2020 | 129 | 2020 |
| Dataset condensation with contrastive signals S Lee, S Chun, S Jung, S Yun, S Yoon International Conference on Machine Learning, 12352-12364, 2022 | 126 | 2022 |
| Fair feature distillation for visual recognition S Jung, D Lee, T Park, T Moon Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 89 | 2021 |
| Learning fair classifiers with partially annotated group labels S Jung, S Chun, T Moon Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 57 | 2022 |
| MEGA: Multi-view semi-supervised clustering of hypergraphs JJ Whang, R Du, S Jung, G Lee, B Drake, Q Liu, S Kang, H Park Proceedings of the VLDB Endowment 13 (5), 698-711, 2020 | 31 | 2020 |
| Re-weighting based group fairness regularization via classwise robust optimization S Jung, T Park, S Chun, T Moon arXiv preprint arXiv:2303.00442, 2023 | 22 | 2023 |
| Adaptive group sparse regularization for continual learning S Jung, H Ahn, S Cha, T Moon arXiv preprint arXiv:2003.13726, 2020 | 9 | 2020 |
| Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline D Lee, S Jung, T Moon The Twelfth International Conference on Learning Representations, 0 | 3 | |
| Listwise reward estimation for offline preference-based reinforcement learning H Choi, S Jung, H Ahn, T Moon arXiv preprint arXiv:2408.04190, 2024 | 2 | 2024 |
| Issues for Continual Learning in the Presence of Dataset Bias D Lee, S Jung, T Moon AAAI Bridge Program on Continual Causality, 92-99, 2023 | 2 | 2023 |
| Continual Learning in the Presence of Spurious Correlation D Lee, S Jung, T Moon arXiv preprint arXiv:2303.11863, 2023 | 2 | 2023 |
| FairDRO: Group fairness regularization via classwise robust optimization T Park, S Jung, S Chun, T Moon Neural Networks 182, 106891, 2025 | | 2025 |
| Do Counterfactually Fair Image Classifiers Satisfy Group Fairness?--A Theoretical and Empirical Study S Jung, S Yu, S Chun, T Moon Advances in Neural Information Processing Systems 37, 56041-56053, 2025 | | 2025 |
| Fair Feature Distillation Using Teacher Models of Larger Architecture S Jung, T Moon 정보과학회논문지 48 (11), 1176-1183, 2021 | | 2021 |
| Supplementary Materials for Fair Feature Distillation for Visual Recognition S Jung, D Lee, T Park, T Moon Learning, 1, 2020 | | 2020 |
| Supplementary Materials for Continual Learning with Node-Importance based Adaptive Group Sparse Regularization S Jung, H Ahn, S Cha, T Moon | | |
Taesup Moon (문태섭)Professor, Department of Electrical and Computer Engineering, Seoul National UniversityE-mailová adresa ověřena na: snu.ac.kr
