| Deep neural networks learn non-smooth functions effectively M Imaizumi, K Fukumizu The 22nd international conference on artificial intelligence and statistics …, 2019 | 93 | 2019 |
| Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality. R Nakada, M Imaizumi J. Mach. Learn. Res. 21, 174:1-174:38, 2020 | 55* | 2020 |
| PCA-based estimation for functional linear regression with functional responses M Imaizumi, K Kato Journal of multivariate analysis 163, 15-36, 2018 | 33 | 2018 |
| On tensor train rank minimization: Statistical efficiency and scalable algorithm M Imaizumi, T Maehara, K Hayashi Advances in Neural Information Processing Systems 30, 2017 | 31 | 2017 |
| Doubly decomposing nonparametric tensor regression M Imaizumi, K Hayashi International Conference on Machine Learning, 727-736, 2016 | 21 | 2016 |
| Finite sample analysis of minimax offline reinforcement learning: Completeness, fast rates and first-order efficiency M Uehara, M Imaizumi, N Jiang, N Kallus, W Sun, T Xie arXiv preprint arXiv:2102.02981, 2021 | 18 | 2021 |
| Tensor decomposition with smoothness M Imaizumi, K Hayashi International Conference on Machine Learning, 1597-1606, 2017 | 14 | 2017 |
| Improved generalization bounds of group invariant/equivariant deep networks via quotient feature spaces A Sannai, M Imaizumi, M Kawano Uncertainty in Artificial Intelligence, 771-780, 2021 | 10* | 2021 |
| Maximum moment restriction for instrumental variable regression R Zhang, M Imaizumi, B Schölkopf, K Muandet arXiv preprint arXiv:2010.07684, 2020 | 8 | 2020 |
| Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces M Imaizumi, K Fukumizu Journal of Machine Learning Research 23, 1-54, 2022 | 7* | 2022 |
| On random subsampling of Gaussian process regression: A graphon-based analysis K Hayashi, M Imaizumi, Y Yoshida International Conference on Artificial Intelligence and Statistics, 2055-2065, 2020 | 7 | 2020 |
| A simple method to construct confidence bands in functional linear regression M Imaizumi, K Kato Statistica Sinica 29 (4), 2055-2081, 2019 | 7 | 2019 |
| Statistically efficient estimation for non-smooth probability densities M Imaizumi, T Maehara, Y Yoshida International Conference on Artificial Intelligence and Statistics, 978-987, 2018 | 5 | 2018 |
| Hypothesis test and confidence analysis with wasserstein distance with general dimension M Imaizumi, H Ota, T Hamaguchi arXiv preprint arXiv:1910.07773, 2019 | 3 | 2019 |
| Understanding higher-order structures in evolving graphs: A simplicial complex based kernel estimation approach M Kaul, M Imaizumi arXiv preprint arXiv:2102.03609, 2021 | 2 | 2021 |
| Best arm identification with a fixed budget under a small gap M Kato, K Ariu, M Imaizumi, M Uehara, M Nomura, C Qin stat 1050, 11, 2022 | 1 | 2022 |
| Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression A Okuno, M Imaizumi arXiv preprint arXiv:2112.00213, 2021 | 1 | 2021 |
| 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 |
| Understanding GANs via generalization analysis for disconnected support M Imaizumi, K Fukumizu | 1 | 2018 |
| Factorized Asymptotic Bayesian Policy Search for POMDPs. M Imaizumi, R Fujimaki IJCAI, 4346-4352, 2017 | 1 | 2017 |
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
