| Adaptive approximation and generalization of deep neural network with intrinsic dimensionality R Nakada, M Imaizumi The Journal of Machine Learning Research 21 (1), 7018-7055, 2020 | 64 | 2020 |
| Adaptive approximation and estimation of deep neural network to intrinsic dimensionality R Nakada, M Imaizumi arXiv preprint arXiv:1907.02177 6, 2019 | 25 | 2019 |
| The power of contrast for feature learning: A theoretical analysis W Ji, Z Deng, R Nakada, J Zou, L Zhang arXiv preprint arXiv:2110.02473, 2021 | 16 | 2021 |
| Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data R Nakada, HI Gulluk, Z Deng, W Ji, J Zou, L Zhang arXiv preprint arXiv:2302.06232, 2023 | 1 | 2023 |
| Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks R Nakada, M Imaizumi arXiv preprint arXiv:2103.00500, 2021 | 1 | 2021 |
| Linear shrinkage estimation of high-dimensional means Y Ikeda, R Nakada, T Kubokawa, MS Srivastava Communications in Statistics-Theory and Methods, 1-17, 2021 | | 2021 |
| 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 |
