Masaaki Imaizumi

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Kenji FukumizuThe Institute of Statistical MathematicsVerificeret mail på ism.ac.jp
Masahiro KatoMizuho-DL Financial Technology Co., Ltd. / The University of TokyoVerificeret mail på g.ecc.u-tokyo.ac.jp
Ryumei NakadaRutgers UniversityVerificeret mail på stat.rutgers.edu
Kohei HayashiResearcher, Preferred NetworksVerificeret mail på preferred.jp
Akiyoshi SannaiKyoto UniversityVerificeret mail på kyoto-u.ac.jp
Rui ZhangThe Australian National University & CSIRO, AUVerificeret mail på anu.edu.au
Krikamol MuandetCISPA - Helmholtz Center for Information SecurityVerificeret mail på cispa.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerificeret mail på tuebingen.mpg.de
Kengo KatoDepartment of Statistics and Data Science, Cornell UniversityVerificeret mail på cornell.edu
Toru KitagawaDepartment of Economics, Brown UniversityVerificeret mail på brown.edu
Takanori MaeharaRoku, inc.Verificeret mail på acm.org
Makoto KawanoThe University of TokyoVerificeret mail på weblab.t.u-tokyo.ac.jp
Hikaru IbayashiUniversity of Southern CaliforniaVerificeret mail på alumni.usc.edu
Yuichi YoshidaNational Institute of InformaticsVerificeret mail på nii.ac.jp
Nan JiangAssociate Professor of Computer Science, UIUCVerificeret mail på illinois.edu
Wen SunAssistant Professor, Cornell UniversityVerificeret mail på cornell.edu
Nathan KallusCornell UniversityVerificeret mail på cornell.edu
Tengyang XieAssistant Professor of Computer Science, University of Wisconsin-MadisonVerificeret mail på cs.wisc.edu
Masatoshi UeharaGenentechVerificeret mail på gene.com
Kenichiro McAlinnTemple UniversityVerificeret mail på temple.edu

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Masaaki Imaizumi

Andre navne今泉允聡

The University of Tokyo / RIKEN AIP

Verificeret mail på g.ecc.u-tokyo.ac.jp - Startside

Statistics Machine Learning


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
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	190*	2020
Deep neural networks learn non-smooth functions effectively M Imaizumi, K Fukumizu Artificial Intelligence and Statistics, 869-878, 2019	181	2019
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	68	2021
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	48	2021
PCA-based estimation for functional linear regression with functional responses M Imaizumi, K Kato Journal of Multivariate Analysis 163, 15-36, 2018	47	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	42	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	41*	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	34*	2022
Doubly decomposing nonparametric tensor regression M Imaizumi, K Hayashi International conference on machine learning, 727-736, 2016	32	2016
Tensor decomposition with smoothness M Imaizumi, K Hayashi International conference on machine learning, 1597-1606, 2017	25	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	24	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
SAN: Inducing metrizability of GAN with discriminative normalized linear layer Y Takida, M Imaizumi, T Shibuya, CH Lai, T Uesaka, N Murata, Y Mitsufuji International Conference on Learning Representations, 2023	10	2023
Inference for projection-based wasserstein distances on finite spaces R Okano, M Imaizumi Statistica Sinica, 2022	10	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	9	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	8	2022
Benign overfitting in time series linear model with over-parameterization S Nakakita, M Imaizumi arXiv preprint arXiv:2204.08369, 2022	7	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

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