Masaaki Imaizumi

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Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
Kohei HayashiResearcher, Preferred NetworksVerified email at preferred.jp
Masahiro KatoThe University of TokyoVerified email at g.ecc.u-tokyo.ac.jp
Akiyoshi SannaiRIKEN / Keio UniversityVerified email at riken.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
Takanori MaeharaMeta, inc.Verified email at acm.org
Makoto KawanoThe University of TokyoVerified email at weblab.t.u-tokyo.ac.jp
Nan JiangAssistant 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 XieUniversity of Wisconsin-Madison, Microsoft ResearchVerified email at cs.wisc.edu
Masatoshi UeharaCornell UniversityVerified email at cornell.edu
Yuichi YoshidaNational Institute of InformaticsVerified email at nii.ac.jp
Kenichiro McAlinnTemple UniversityVerified email at temple.edu
Kaito AriuCyberAgent, Inc. / KTH / kaitoariu@gmail.comVerified email at kth.se
Hirofumi OtaRutgers UniversityVerified email at rutgers.edu
Shota YasuiCyberAgent, inc.Verified email at cyberagent.co.jp

Masaaki Imaizumi

Other names今泉允聡

The University of Tokyo

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

Statistics Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Deep neural networks learn non-smooth functions effectively M Imaizumi, K Fukumizu Artificial Intelligence and Statistics, 869-878, 2019	127	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	112*	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	50	2021
PCA-based estimation for functional linear regression with functional responses M Imaizumi, K Kato Journal of multivariate analysis 163, 15-36, 2018	38	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	35	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	34	2021
Instrumental variable regression via kernel maximum moment loss R Zhang, M Imaizumi, B Schölkopf, K Muandet Journal of Causal Inference 11 (1), 20220073, 2023	26*	2023
Doubly decomposing nonparametric tensor regression M Imaizumi, K Hayashi International Conference on Machine Learning, 727-736, 2016	26	2016
Tensor decomposition with smoothness M Imaizumi, K Hayashi International Conference on Machine Learning, 1597-1606, 2017	18	2017
Advantage of deep neural networks for estimating functions with singularity on hypersurfaces M Imaizumi, K Fukumizu The Journal of Machine Learning Research 23 (1), 4772-4825, 2022	16*	2022
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	15	2020
A simple method to construct confidence bands in functional linear regression M Imaizumi, K Kato Statistica Sinica 29 (4), 2055-2081, 2019	12	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	7	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	6	2021
Statistically efficient estimation for non-smooth probability densities M Imaizumi, T Maehara, Y Yoshida International Conference on Artificial Intelligence and Statistics, 978-987, 2018	6	2018
Inference for projection-based wasserstein distances on finite spaces R Okano, M Imaizumi Statistica Sinica, 2022	3	2022
Fréchet kernel for trajectory data analysis K Takeuchi, M Imaizumi, S Kanda, Y Tabei, K Fujii, K Yoda, M Ishihata, ... Proceedings of the 29th International Conference on Advances in Geographic …, 2021	3	2021
Minimum sharpness: Scale-invariant parameter-robustness of neural networks H Ibayashi, T Hamaguchi, M Imaizumi arXiv preprint arXiv:2106.12612, 2021	3	2021
Dimension-free bounds for sum of dependent matrices and operators with heavy-tailed distribution S Nakakita, P Alquier, M Imaizumi arXiv preprint arXiv:2210.09756, 2022	2	2022
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	2	2022

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