Masaaki Imaizumi

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Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Masahiro KatoMizuho-DL Financial Technology Co., Ltd. / The University of TokyoVerified email at g.ecc.u-tokyo.ac.jp
Ryumei NakadaRutgers UniversityVerified email at stat.rutgers.edu
Kohei HayashiResearcher, Preferred NetworksVerified email at preferred.jp
Akiyoshi SannaiKyoto UniversityVerified email at kyoto-u.ac.jp
Rui ZhangThe Australian National University & CSIRO, AUVerified email at anu.edu.au
Krikamol MuandetCISPA - Helmholtz Center for Information SecurityVerified email at cispa.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Kengo KatoDepartment of Statistics and Data Science, Cornell UniversityVerified email at cornell.edu
Toru KitagawaDepartment of Economics, Brown UniversityVerified email at brown.edu
Takanori MaeharaRoku, inc.Verified email at acm.org
Makoto KawanoThe University of TokyoVerified email at weblab.t.u-tokyo.ac.jp
Hikaru IbayashiUniversity of Southern CaliforniaVerified email at alumni.usc.edu
Nan JiangAssociate Professor of Computer Science, UIUCVerified email at illinois.edu
Wen SunAssistant Professor, Cornell UniversityVerified email at cornell.edu
Nathan KallusCornell UniversityVerified email at cornell.edu
Tengyang XieAssistant Professor of Computer Science, University of Wisconsin-MadisonVerified email at cs.wisc.edu
Masatoshi UeharaGenentechVerified email at gene.com
Yuichi YoshidaNational Institute of InformaticsVerified email at nii.ac.jp
Kenichiro McAlinnTemple UniversityVerified email at temple.edu

Masaaki Imaizumi

Other names今泉允聡

The University of Tokyo / RIKEN AIP

Verified email at g.ecc.u-tokyo.ac.jp - Homepage

Statistics Machine Learning


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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	189*	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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