Masaaki Imaizumi

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i10 指標	15	15

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Kenji FukumizuThe Institute of Statistical Mathematics確認したメールアドレス: ism.ac.jp
Masahiro KatoMizuho-DL Financial Technology Co., Ltd. / The University of Tokyo確認したメールアドレス: g.ecc.u-tokyo.ac.jp
Ryumei NakadaRutgers University確認したメールアドレス: stat.rutgers.edu
Kohei HayashiResearcher, Preferred Networks確認したメールアドレス: preferred.jp
Akiyoshi SannaiKyoto University確認したメールアドレス: kyoto-u.ac.jp
Rui ZhangThe Australian National University & CSIRO, AU確認したメールアドレス: anu.edu.au
Krikamol MuandetCISPA - Helmholtz Center for Information Security確認したメールアドレス: cispa.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH確認したメールアドレス: tuebingen.mpg.de
Kengo KatoDepartment of Statistics and Data Science, Cornell University確認したメールアドレス: cornell.edu
Toru KitagawaDepartment of Economics, Brown University確認したメールアドレス: brown.edu
Takanori MaeharaRoku, inc.確認したメールアドレス: acm.org
Makoto KawanoThe University of Tokyo確認したメールアドレス: weblab.t.u-tokyo.ac.jp
Hikaru IbayashiUniversity of Southern California確認したメールアドレス: alumni.usc.edu
Nan JiangAssociate Professor of Computer Science, UIUC確認したメールアドレス: illinois.edu
Wen SunAssistant Professor, Cornell University確認したメールアドレス: cornell.edu
Nathan KallusCornell University確認したメールアドレス: cornell.edu
Tengyang XieAssistant Professor of Computer Science, University of Wisconsin-Madison確認したメールアドレス: cs.wisc.edu
Masatoshi UeharaGenentech確認したメールアドレス: gene.com
Yuichi YoshidaNational Institute of Informatics確認したメールアドレス: nii.ac.jp
Kenichiro McAlinnTemple University確認したメールアドレス: temple.edu

フォロー

Masaaki Imaizumi

その他の名前今泉允聡

The University of Tokyo / RIKEN AIP

確認したメールアドレス: g.ecc.u-tokyo.ac.jp - ホームページ

Statistics Machine Learning


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	47	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	23	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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