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ORCIDMasaaki Imaizumi
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2020 – today
- 2024
[j6]Ryo Okano, Masaaki Imaizumi:
Distribution-on-distribution regression with Wasserstein metric: Multivariate Gaussian case. J. Multivar. Anal. 203: 105334 (2024)- [c15]Yuhta Takida, Masaaki Imaizumi, Takashi Shibuya, Chieh-Hsin Lai, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji:
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer. ICLR 2024 - [c14]Shuhei Kashiwamura, Ayaka Sakata, Masaaki Imaizumi:
Effect of Weight Quantization on Learning Models by Typical Case Analysis. ISIT 2024: 357-362 - [i25]Shuhei Kashiwamura, Ayaka Sakata, Masaaki Imaizumi:
Effect of Weight Quantization on Learning Models by Typical Case Analysis. CoRR abs/2401.17269 (2024) - [i24]Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi:
Automatic Domain Adaptation by Transformers in In-Context Learning. CoRR abs/2405.16819 (2024) - [i23]Naoki Yoshida, Shogo Nakakita, Masaaki Imaizumi:
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution. CoRR abs/2406.16032 (2024) - 2023
- [j5]Masaaki Imaizumi
, Johannes Schmidt-Hieber:
On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization. IEEE Trans. Inf. Theory 69(2): 1203-1223 (2023) - [c13]Masahiro Kato, Masaaki Imaizumi, Kentaro Minami:
Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics. AISTATS 2023: 5271-5298 - [c12]Junpei Komiyama, Masaaki Imaizumi:
High-dimensional Contextual Bandit Problem without Sparsity. NeurIPS 2023 - [i22]Yuhta Takida, Masaaki Imaizumi, Chieh-Hsin Lai, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji:
Adversarially Slicing Generative Networks: Discriminator Slices Feature for One-Dimensional Optimal Transport. CoRR abs/2301.12811 (2023) - [i21]Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, Toru Kitagawa:
Asymptotically Minimax Optimal Fixed-Budget Best Arm Identification for Expected Simple Regret Minimization. CoRR abs/2302.02988 (2023) - [i20]Junpei Komiyama, Masaaki Imaizumi:
High-dimensional Contextual Bandit Problem without Sparsity. CoRR abs/2306.11017 (2023) - [i19]Masaaki Imaizumi:
Sup-Norm Convergence of Deep Neural Network Estimator for Nonparametric Regression by Adversarial Training. CoRR abs/2307.04042 (2023) - [i18]Masahiro Kato, Akari Ohda, Masaaki Imaizumi, Kenichiro McAlinn:
Synthetic Control Methods by Density Matching under Implicit Endogeneity. CoRR abs/2307.11127 (2023) - [i17]Masahiro Kato, Masaaki Imaizumi:
CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression. CoRR abs/2310.16819 (2023) - 2022
- [j4]Masaaki Imaizumi, Kenji Fukumizu:
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces. J. Mach. Learn. Res. 23: 111:1-111:54 (2022) - [j3]Masaaki Imaizumi, Hirofumi Ota, Takuo Hamaguchi:
Hypothesis Test and Confidence Analysis With Wasserstein Distance on General Dimension. Neural Comput. 34(6): 1448-1487 (2022) - [c11]Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, Haruo Kakehi:
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting. ICLR 2022 - [i16]Masahiro Kato, Kaito Ariu, Masaaki Imaizumi, Masatoshi Uehara, Masahiro Nomura, Chao Qin:
Optimal Fixed-Budget Best Arm Identification using the Augmented Inverse Probability Weighting Estimator in Two-Armed Gaussian Bandits with Unknown Variances. CoRR abs/2201.04469 (2022) - [i15]Masaaki Imaizumi, Johannes Schmidt-Hieber:
On generalization bounds for deep networks based on loss surface implicit regularization. CoRR abs/2201.04545 (2022) - [i14]Masahiro Kato, Masaaki Imaizumi, Kentaro Minami:
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics. CoRR abs/2201.13127 (2022) - [i13]Masahiro Kato, Masaaki Imaizumi:
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression. CoRR abs/2202.05245 (2022) - [i12]Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, Toru Kitagawa:
Semiparametric Best Arm Identification with Contextual Information. CoRR abs/2209.07330 (2022) - 2021
- [c10]Koh Takeuchi, Masaaki Imaizumi, Shunsuke Kanda, Yasuo Tabei, Keisuke Fujii, Ken Yoda, Masakazu Ishihata, Takuya Maekawa:
Fréchet Kernel for Trajectory Data Analysis. SIGSPATIAL/GIS 2021: 221-224 - [c9]Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano:
Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces. UAI 2021: 771-780 - [i11]Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie:
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. CoRR abs/2102.02981 (2021) - [i10]Manohar Kaul, Masaaki Imaizumi:
Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach. CoRR abs/2102.03609 (2021) - [i9]Ryumei Nakada, Masaaki Imaizumi:
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks. CoRR abs/2103.00500 (2021) - [i8]Rui Zhang, Krikamol Muandet, Bernhard Schölkopf, Masaaki Imaizumi:
Instrument Space Selection for Kernel Maximum Moment Restriction. CoRR abs/2106.03340 (2021) - [i7]Hikaru Ibayashi, Takuo Hamaguchi, Masaaki Imaizumi:
Minimum sharpness: Scale-invariant parameter-robustness of neural networks. CoRR abs/2106.12612 (2021) - [i6]Hikaru Ibayashi, Masaaki Imaizumi:
Quasi-potential theory for escape problem: Quantitative sharpness effect on SGD's escape from local minima. CoRR abs/2111.04004 (2021) - 2020
- [j2]Ryumei Nakada, Masaaki Imaizumi:
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality. J. Mach. Learn. Res. 21: 174:1-174:38 (2020) - [c8]Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida:
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis. AISTATS 2020: 2055-2065 - [i5]Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet:
Maximum Moment Restriction for Instrumental Variable Regression. CoRR abs/2010.07684 (2020) - [i4]Masaaki Imaizumi, Kenji Fukumizu:
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Curves. CoRR abs/2011.02256 (2020)
2010 – 2019
- 2019
- [c7]Masaaki Imaizumi, Kenji Fukumizu:
Deep Neural Networks Learn Non-Smooth Functions Effectively. AISTATS 2019: 869-878 - [i3]Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida:
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis. CoRR abs/1901.09541 (2019) - [i2]Ryumei Nakada, Masaaki Imaizumi:
Adaptive Approximation and Estimation of Deep Neural Network to Intrinsic Dimensionality. CoRR abs/1907.02177 (2019) - [i1]Akiyoshi Sannai, Masaaki Imaizumi:
Improved Generalization Bound of Permutation Invariant Deep Neural Networks. CoRR abs/1910.06552 (2019) - 2018
- [j1]Masaaki Imaizumi, Kengo Kato:
PCA-based estimation for functional linear regression with functional responses. J. Multivar. Anal. 163: 15-36 (2018) - [c6]Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida:
Statistically Efficient Estimation for Non-Smooth Probability Densities. AISTATS 2018: 978-987 - 2017
- [c5]Masaaki Imaizumi, Kohei Hayashi:
Tensor Decomposition with Smoothness. ICML 2017: 1597-1606 - [c4]Masaaki Imaizumi, Ryohei Fujimaki:
Factorized Asymptotic Bayesian Policy Search for POMDPs. IJCAI 2017: 4346-4352 - [c3]Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi:
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm. NIPS 2017: 3930-3939 - 2016
- [c2]Masaaki Imaizumi, Kohei Hayashi:
Doubly Decomposing Nonparametric Tensor Regression. ICML 2016: 727-736
1990 – 1999
- 1997
- [c1]Mamoru Tanaka, Kenya Jin'no, Jun'ichi Miyata, Masaaki Imaizumi, Toshiaki Shingu, Hiroshi Inoue:
Resolutionable cellular neural networks. ICNN 1997: 1535-1541
Coauthor Index
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