| Learning fair representation with a parametric integral probability metric D Kim, K Kim, I Kong, I Ohn, Y Kim International Conference on Machine Learning, 11074-11101, 2022 | 18 | 2022 |
| Improving adversarial robustness by putting more regularizations on less robust samples D Yang, I Kong, Y Kim International Conference on Machine Learning, 39331-39348, 2023 | 13* | 2023 |
| Masked Bayesian neural networks: Theoretical guarantee and its posterior inference I Kong, D Yang, J Lee, I Ohn, G Baek, Y Kim International conference on machine learning, 17462-17491, 2023 | 6 | 2023 |
| Covariate balancing using the integral probability metric for causal inference I Kong, Y Park, J Jung, K Lee, Y Kim International Conference on Machine Learning, 17430-17461, 2023 | 6 | 2023 |
| Enhancing adversarial robustness in low-label regime via adaptively weighted regularization and knowledge distillation D Yang, I Kong, Y Kim Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 5 | 2023 |
| Tensor Product Neural Networks for Functional ANOVA Model S Park, I Kong, Y Choi, C Park, Y Kim arXiv preprint arXiv:2502.15215, 2025 | | 2025 |
| Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics I Kong, K Kim, Y Kim IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025 | | 2025 |
| Fairness Through Matching K Kim, I Kong, J Lee, M Chae, S Park, Y Kim arXiv preprint arXiv:2501.02793, 2025 | | 2025 |
| Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights I Kong, Y Kim arXiv preprint arXiv:2403.14225, 2024 | | 2024 |
