| Lipschitz-certifiable training with a tight outer bound S Lee, J Lee, S Park Advances in Neural Information Processing Systems 33, 16891-16902, 2020 | 48 | 2020 |
| HE-friendly algorithm for privacy-preserving SVM training S Park, J Byun, J Lee, JH Cheon, J Lee IEEE Access 8, 57414-57425, 2020 | 45 | 2020 |
| Semi-supervised distributed representations of documents for sentiment analysis S Park, J Lee, K Kim Neural Networks 119, 139-150, 2019 | 32 | 2019 |
| Predicting market impact costs using nonparametric machine learning models S Park, J Lee, Y Son PLoS One 11 (2), e0150243, 2016 | 27 | 2016 |
| Privacy-preserving fair learning of support vector machine with homomorphic encryption S Park, J Byun, J Lee Proceedings of the ACM Web Conference 2022, 3572-3583, 2022 | 20 | 2022 |
| Fairness audit of machine learning models with confidential computing S Park, S Kim, Y Lim Proceedings of the ACM Web Conference 2022, 3488-3499, 2022 | 19 | 2022 |
| Efficient differentially private kernel support vector classifier for multi-class classification J Park, Y Choi, J Byun, J Lee, S Park Information Sciences 619, 889-907, 2023 | 15 | 2023 |
| Security-preserving Support Vector Machine with Fully Homomorphic Encryption. S Park, J Lee, JH Cheon, J Lee, J Kim, J Byun SafeAI@ AAAI 2301, 2019 | 12 | 2019 |
| Learning representative exemplars using one-class Gaussian process regression Y Son, S Lee, S Park, J Lee Pattern Recognition 74, 185-197, 2018 | 9 | 2018 |
| Information‐Based Boundary Equilibrium Generative Adversarial Networks with Interpretable Representation Learning J Hah, W Lee, J Lee, S Park Computational Intelligence and Neuroscience 2018 (1), 6465949, 2018 | 7 | 2018 |
| Inductive ensemble clustering using kernel support matching S Park, J Hah, J Lee Electronics Letters 53 (25), 1625-1626, 2017 | 7 | 2017 |
| Learning of indiscriminate distributions of document embeddings for domain adaptation S Park, W Lee, J Lee Intelligent Data Analysis 23 (4), 779-797, 2019 | 6 | 2019 |
| Efficient homomorphic encryption framework for privacy-preserving regression J Byun, S Park, Y Choi, J Lee Applied Intelligence 53 (9), 10114-10129, 2023 | 5 | 2023 |
| Privacy‐preserving evaluation for support vector clustering J Byun, J Lee, S Park Electronics Letters 57 (2), 61-64, 2021 | 5 | 2021 |
| Defensive denoising methods against adversarial attack S Lee, J Lee ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2018 | 5 | 2018 |
| Instance weighting domain adaptation using distance kernel W Lee, J Lee, S Park Industrial Engineering & Management Systems 17 (2), 334-340, 2018 | 5 | 2018 |
| The SaTML'24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability S Casper, J Yun, J Baek, Y Jung, M Kim, K Kwon, S Park, H Moore, ... arXiv preprint arXiv:2404.02949, 2024 | 2 | 2024 |
| Fair sampling in diffusion models through switching mechanism Y Choi, J Park, H Kim, J Lee, S Park Proceedings of the AAAI Conference on Artificial Intelligence 38 (20), 21995 …, 2024 | 2 | 2024 |
| Stability analysis of denoising autoencoders based on dynamical projection system S Park, J Lee IEEE Transactions on Knowledge and Data Engineering 33 (8), 3155-3159, 2020 | 2 | 2020 |
| Privacy-preserving inference resistant to model extraction attacks J Byun, Y Choi, J Lee, S Park Expert Systems with Applications 256, 124830, 2024 | | 2024 |
Jaewook LeeProfessor of Industrial Engineering, Seoul National University在 snu.ac.kr 的电子邮件经过验证
