| Gradient inversion with generative image prior J Jeon, K Lee, S Oh, J Ok Advances in neural information processing systems 34, 29898-29908, 2021 | 176 | 2021 |
| Exploration in structured reinforcement learning J Ok, A Proutiere, D Tranos Advances in Neural Information Processing Systems 31, 2018 | 80 | 2018 |
| Optimal rate sampling in 802.11 systems: Theory, design, and implementation R Combes, J Ok, A Proutiere, D Yun, Y Yi IEEE Transactions on Mobile Computing 18 (5), 1145-1158, 2018 | 66 | 2018 |
| Optimal rate sampling in 802.11 systems R Combes, A Proutiere, D Yun, J Ok, Y Yi IEEE INFOCOM 2014-IEEE Conference on Computer Communications, 2760-2767, 2014 | 55 | 2014 |
| Optimality of belief propagation for crowdsourced classification J Ok, S Oh, J Shin, Y Yi International Conference on Machine Learning, 535-544, 2016 | 52 | 2016 |
| Embedding of virtual network requests over static wireless multihop networks D Yun, J Ok, B Shin, S Park, Y Yi Computer Networks 57 (5), 1139-1152, 2013 | 45 | 2013 |
| Towards sequence-level training for visual tracking M Kim, S Lee, J Ok, B Han, M Cho European Conference on Computer Vision, 534-551, 2022 | 44 | 2022 |
| Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning H Hu, S Wang, T Dong, M Xue 2024 IEEE Symposium on Security and Privacy (SP), 3257-3275, 2024 | 27 | 2024 |
| On maximizing diffusion speed in social networks: impact of random seeding and clustering J Ok, Y Jin, J Shin, Y Yi The 2014 ACM international conference on Measurement and modeling of …, 2014 | 25 | 2014 |
| Combating label distribution shift for active domain adaptation S Hwang, S Lee, S Kim, J Ok, S Kwak European Conference on Computer Vision, 549-566, 2022 | 24 | 2022 |
| Few-shot unlearning by model inversion Y Yoon, J Nam, H Yun, J Lee, D Kim, J Ok arXiv preprint arXiv:2205.15567, 2022 | 21 | 2022 |
| On Maximizing Diffusion Speed Over Social Networks With Strategic Users J Ok, Y Jin, J Shin, Y Yi IEEE/ACM Transactions on Networkings 24 (6), 2016 | 17 | 2016 |
| Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning B Ko, J Ok arXiv preprint arXiv:2206.00518, 2022 | 16* | 2022 |
| Leveraging proxy of training data for test-time adaptation J Kang, N Kim, D Kwon, J Ok, S Kwak | 14 | 2023 |
| Learning continuous representation of audio for arbitrary scale super resolution J Kim, Y Lee, S Hong, J Ok ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 13 | 2022 |
| Combinatorial pure exploration with continuous and separable reward functions and its applications. W Huang, J Ok, L Li, W Chen IJCAI, 2291-2297, 2018 | 13 | 2018 |
| Iterative bayesian learning for crowdsourced regression J Ok, S Oh, Y Jang, J Shin, Y Yi The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 11* | 2019 |
| Adaptive superpixel for active learning in semantic segmentation H Kim, M Oh, S Hwang, S Kwak, J Ok Proceedings of the IEEE/CVF International Conference on Computer Vision, 943-953, 2023 | 10 | 2023 |
| On the impact of global information on diffusion of innovations over social networks Y Jin, J Ok, Y Yi, J Shin 2013 Proceedings IEEE INFOCOM, 3267-3272, 2013 | 10 | 2013 |
| Medbn: Robust test-time adaptation against malicious test samples H Park, J Hwang, S Mun, S Park, J Ok Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 9 | 2024 |
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