Masaaki Imaizumi
Masaaki Imaizumi
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Deep neural networks learn non-smooth functions effectively
M Imaizumi, K Fukumizu
The 22nd international conference on artificial intelligence and statistics …, 2019
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality.
R Nakada, M Imaizumi
J. Mach. Learn. Res. 21 (174), 1-38, 2020
PCA-based estimation for functional linear regression with functional responses
M Imaizumi, K Kato
Journal of multivariate analysis 163, 15-36, 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
Doubly decomposing nonparametric tensor regression
M Imaizumi, K Hayashi
International Conference on Machine Learning, 727-736, 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
Tensor decomposition with smoothness
M Imaizumi, K Hayashi
International Conference on Machine Learning, 1597-1606, 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
Maximum moment restriction for instrumental variable regression
R Zhang, M Imaizumi, B Schölkopf, K Muandet
arXiv preprint arXiv:2010.07684, 2020
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
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
A simple method to construct confidence bands in functional linear regression
M Imaizumi, K Kato
Statistica Sinica 29 (4), 2055-2081, 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
Hypothesis Test and Confidence Analysis with Wasserstein Distance on General Dimension
M Imaizumi, H Ota, T Hamaguchi
Neural Computation 34 (6), 1448-1487, 2022
Inference for Projection-Based Wasserstein Distances on Finite Spaces
R Okano, M Imaizumi
arXiv preprint arXiv:2202.05495, 2022
Understanding higher-order structures in evolving graphs: A simplicial complex based kernel estimation approach
M Kaul, M Imaizumi
arXiv preprint arXiv:2102.03609, 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
Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression
A Okuno, M Imaizumi
arXiv preprint arXiv:2112.00213, 2021
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
M Kato, M Imaizumi, K McAlinn, S Yasui, H Kakehi
International Conference on Learning Representations, 2021
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks
R Nakada, M Imaizumi
arXiv preprint arXiv:2103.00500, 2021
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