| Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems J Kim, K Yamamoto, K Oko, Z Yang, T Suzuki The Twelfth International Conference on Learning Representations, 2024 | 9 | 2024 |
| Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape J Kim, T Suzuki International Conference on Machine Learning, 2024 | 8 | 2024 |
| Reeb flows without simple global surfaces of section J Kim, Y Kim, O van Koert Involve, a Journal of Mathematics 15 (5), 813-842, 2023 | 3 | 2023 |
| Transformers are Minimax Optimal Nonparametric In-Context Learners J Kim, T Nakamaki, T Suzuki arXiv preprint arXiv:2408.12186, 2024 | 1 | 2024 |
| Hessian Based Smoothing Splines for Manifold Learning J Kim arXiv preprint arXiv:2302.05025, 2023 | 1 | 2023 |
| -Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence J Kim, J Kwon, M Cho, H Lee, JH Won The Twelfth International Conference on Learning Representations, 2024 | | 2024 |
| A Central Limit Theorem for Rosen Continued Fractions J Kim, K Choi arXiv preprint arXiv:2009.02047, 2020 | | 2020 |
