| Adaptive approximation and generalization of deep neural network with intrinsic dimensionality R Nakada, M Imaizumi Journal of Machine Learning Research 21 (174), 1-38, 2020 | 143* | 2020 |
| The power of contrast for feature learning: A theoretical analysis W Ji, Z Deng, R Nakada, J Zou, L Zhang Journal of Machine Learning Research 24 (330), 1-78, 2023 | 30 | 2023 |
| Understanding multimodal contrastive learning and incorporating unpaired data R Nakada, HI Gulluk, Z Deng, W Ji, J Zou, L Zhang International Conference on Artificial Intelligence and Statistics, 4348-4380, 2023 | 15 | 2023 |
| Safeguarding data in multimodal ai: A differentially private approach to clip training A Huang, P Liu, R Nakada, L Zhang, W Zhang arXiv preprint arXiv:2306.08173, 2023 | 2 | 2023 |
| Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks R Nakada, M Imaizumi arXiv preprint arXiv:2103.00500, 2021 | 2 | 2021 |
| Contrastive Learning on Multimodal Analysis of Electronic Health Records T Cai, F Huang, R Nakada, L Zhang, D Zhou arXiv preprint arXiv:2403.14926, 2024 | | 2024 |
| Linear shrinkage estimation of high-dimensional means Y Ikeda, R Nakada, T Kubokawa, MS Srivastava Communications in Statistics-Theory and Methods 52 (13), 4444-4460, 2023 | | 2023 |
| Linear shrinkage estimation of the variance of a distribution with unknown mean Y Ikeda, R Nakada, T Kubokawa, MS Srivastava Communications in Statistics-Theory and Methods 50 (9), 2039-2047, 2021 | | 2021 |
| Shrinkage estimation with singular priors and an application to small area estimation R Nakada, T Kubokawa, M Ghosh, S Karmakar Journal of Multivariate Analysis 183, 104726, 2021 | | 2021 |
