Masaaki Imaizumi

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Kenji FukumizuThe Institute of Statistical MathematicsDirección de correo verificada de ism.ac.jp
Masahiro KatoMizuho-DL Financial Technology Co., Ltd. / The University of TokyoDirección de correo verificada de g.ecc.u-tokyo.ac.jp
Kohei HayashiResearcher, Preferred NetworksDirección de correo verificada de preferred.jp
Ryumei NakadaRutgers UniversityDirección de correo verificada de stat.rutgers.edu
Akiyoshi SannaiKyoto UniversityDirección de correo verificada de kyoto-u.ac.jp
Rui ZhangThe Australian National University & CSIRO, AUDirección de correo verificada de anu.edu.au
Krikamol MuandetCISPA - Helmholtz Center for Information SecurityDirección de correo verificada de cispa.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHDirección de correo verificada de tuebingen.mpg.de
Kengo KatoDepartment of Statistics and Data Science, Cornell UniversityDirección de correo verificada de cornell.edu
Toru KitagawaDepartment of Economics, Brown UniversityDirección de correo verificada de brown.edu
Takanori MaeharaRoku, inc.Dirección de correo verificada de acm.org
Makoto KawanoThe University of TokyoDirección de correo verificada de weblab.t.u-tokyo.ac.jp
Hikaru IbayashiUniversity of Southern CaliforniaDirección de correo verificada de alumni.usc.edu
Nan JiangAssociate Professor of Computer Science, UIUCDirección de correo verificada de illinois.edu
Wen SunAssistant Professor, Cornell UniversityDirección de correo verificada de cornell.edu
Nathan KallusCornell UniversityDirección de correo verificada de cornell.edu
Tengyang XieAssistant Professor of Computer Science, University of Wisconsin-MadisonDirección de correo verificada de cs.wisc.edu
Masatoshi UeharaGenentechDirección de correo verificada de gene.com
Yuichi YoshidaNational Institute of InformaticsDirección de correo verificada de nii.ac.jp
Kenichiro McAlinnTemple UniversityDirección de correo verificada de temple.edu

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Masaaki Imaizumi

Otros nombres今泉允聡

The University of Tokyo / RIKEN AIP

Dirección de correo verificada de g.ecc.u-tokyo.ac.jp - Página principal

Statistics Machine Learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Deep neural networks learn non-smooth functions effectively M Imaizumi, K Fukumizu Artificial Intelligence and Statistics, 869-878, 2019	169	2019
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	167*	2020
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	62	2021
PCA-based estimation for functional linear regression with functional responses M Imaizumi, K Kato Journal of Multivariate Analysis 163, 15-36, 2018	45	2018
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	44	2021
On tensor train rank minimization: Statistical efficiency and scalable algorithm M Imaizumi, T Maehara, K Hayashi Advances in Neural Information Processing Systems 30, 2017	40	2017
Instrumental variable regression via kernel maximum moment loss R Zhang, M Imaizumi, B Schölkopf, K Muandet Journal of Causal Inference 11 (1), 20220073, 2023	36*	2023
Advantage of deep neural networks for estimating functions with singularity on hypersurfaces M Imaizumi, K Fukumizu Journal of Machine Learning Research 23 (1), 4772-4825, 2022	31*	2022
Doubly decomposing nonparametric tensor regression M Imaizumi, K Hayashi International conference on machine learning, 727-736, 2016	31	2016
Tensor decomposition with smoothness M Imaizumi, K Hayashi International conference on machine learning, 1597-1606, 2017	22	2017
On random subsampling of Gaussian process regression: A graphon-based analysis K Hayashi, M Imaizumi, Y Yoshida Artificial Intelligence and Statistics, 2055-2065, 2020	18	2020
A simple method to construct confidence bands in functional linear regression M Imaizumi, K Kato Statistica Sinica 29 (4), 2055-2081, 2019	15	2019
Hypothesis test and confidence analysis with wasserstein distance on general dimension M Imaizumi, H Ota, T Hamaguchi Neural Computation 34 (6), 1448-1487, 2022	12	2022
Inference for projection-based wasserstein distances on finite spaces R Okano, M Imaizumi Statistica Sinica, 2022	8	2022
Learning causal models from conditional moment restrictions by importance weighting M Kato, M Imaizumi, K McAlinn, H Kakehi, S Yasui International Conference on Learning Representation, 2021	8	2021
On generalization bounds for deep networks based on loss surface implicit regularization M Imaizumi, J Schmidt-Hieber IEEE Transactions on Information Theory 69 (2), 1203-1223, 2022	6	2022
Benign overfitting in time series linear model with over-parameterization S Nakakita, M Imaizumi arXiv preprint arXiv:2204.08369, 2022	6	2022
Exponential escape efficiency of SGD from sharp minima in non-stationary regime H Ibayashi, M Imaizumi arXiv preprint arXiv:2111.04004, 2021	6	2021
Statistically efficient estimation for non-smooth probability densities M Imaizumi, T Maehara, Y Yoshida Artificial Intelligence and Statistics, 978-987, 2018	6	2018
SAN: Inducing metrizability of GAN with discriminative normalized linear layer Y Takida, M Imaizumi, T Shibuya, CH Lai, T Uesaka, N Murata, Y Mitsufuji arXiv preprint arXiv:2301.12811, 2023	5	2023

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