Masaaki Imaizumi

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 44

Top Topics

Linear Regression

Contextual Bandit

Regret Minimization

Top Venues

J. Multivar. Anal.

J. Mach. Learn. Res.

Publications

Naoki Yoshida, Shogo Nakakita, Masaaki Imaizumi
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution. CoRR (2024)
Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi
Automatic Domain Adaptation by Transformers in In-Context Learning. CoRR (2024)
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)
Shuhei Kashiwamura, Ayaka Sakata, Masaaki Imaizumi
Effect of Weight Quantization on Learning Models by Typical Case Analysis. CoRR (2024)
Ryo Okano, Masaaki Imaizumi
Distribution-on-distribution regression with Wasserstein metric: Multivariate Gaussian case. J. Multivar. Anal. 203 (2024)
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 (2023)
Masahiro Kato, Akari Ohda, Masaaki Imaizumi, Kenichiro McAlinn
Synthetic Control Methods by Density Matching under Implicit Endogeneity. CoRR (2023)
Masaaki Imaizumi
Sup-Norm Convergence of Deep Neural Network Estimator for Nonparametric Regression by Adversarial Training. CoRR (2023)
Junpei Komiyama, Masaaki Imaizumi
High-dimensional Contextual Bandit Problem without Sparsity. NeurIPS (2023)
Masahiro Kato, Masaaki Imaizumi
CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression. CoRR (2023)
Masaaki Imaizumi, Johannes Schmidt-Hieber
On Generalization Bounds for Deep Networks Based on Loss Surface Implicit Regularization. IEEE Trans. Inf. Theory 69 (2) (2023)
Junpei Komiyama, Masaaki Imaizumi
High-dimensional Contextual Bandit Problem without Sparsity. CoRR (2023)
Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, Toru Kitagawa
Asymptotically Minimax Optimal Fixed-Budget Best Arm Identification for Expected Simple Regret Minimization. CoRR (2023)
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)
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 (2022)
Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, Haruo Kakehi
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting. ICLR (2022)
Masaaki Imaizumi, Hirofumi Ota, Takuo Hamaguchi
Hypothesis Test and Confidence Analysis With Wasserstein Distance on General Dimension. Neural Comput. 34 (6) (2022)
Masaaki Imaizumi, Johannes Schmidt-Hieber
On generalization bounds for deep networks based on loss surface implicit regularization. CoRR (2022)
Masahiro Kato, Masaaki Imaizumi
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression. CoRR (2022)
Masaaki Imaizumi, Kenji Fukumizu
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces. J. Mach. Learn. Res. 23 (2022)
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 (2022)
Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, Toru Kitagawa
Semiparametric Best Arm Identification with Contextual Information. CoRR (2022)
Manohar Kaul, Masaaki Imaizumi
Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach. CoRR (2021)
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)
Ryumei Nakada, Masaaki Imaizumi
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks. CoRR (2021)
Rui Zhang, Krikamol Muandet, Bernhard Schölkopf, Masaaki Imaizumi
Instrument Space Selection for Kernel Maximum Moment Restriction. CoRR (2021)
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 (2021)
Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano
Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces. UAI (2021)
Hikaru Ibayashi, Takuo Hamaguchi, Masaaki Imaizumi
Minimum sharpness: Scale-invariant parameter-robustness of neural networks. CoRR (2021)
Hikaru Ibayashi, Masaaki Imaizumi
Quasi-potential theory for escape problem: Quantitative sharpness effect on SGD's escape from local minima. CoRR (2021)
Ryumei Nakada, Masaaki Imaizumi
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality. J. Mach. Learn. Res. 21 (2020)
Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis. AISTATS (2020)
Masaaki Imaizumi, Kenji Fukumizu
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Curves. CoRR (2020)
Rui Zhang, Masaaki Imaizumi, Bernhard Schölkopf, Krikamol Muandet
Maximum Moment Restriction for Instrumental Variable Regression. CoRR (2020)
Akiyoshi Sannai, Masaaki Imaizumi
Improved Generalization Bound of Permutation Invariant Deep Neural Networks. CoRR (2019)
Ryumei Nakada, Masaaki Imaizumi
Adaptive Approximation and Estimation of Deep Neural Network to Intrinsic Dimensionality. CoRR (2019)
Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis. CoRR (2019)
Masaaki Imaizumi, Kenji Fukumizu
Deep Neural Networks Learn Non-Smooth Functions Effectively. AISTATS (2019)
Masaaki Imaizumi, Kengo Kato
PCA-based estimation for functional linear regression with functional responses. J. Multivar. Anal. 163 (2018)
Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida
Statistically Efficient Estimation for Non-Smooth Probability Densities. AISTATS (2018)
Masaaki Imaizumi, Ryohei Fujimaki
Factorized Asymptotic Bayesian Policy Search for POMDPs. IJCAI (2017)
Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm. NIPS (2017)
Masaaki Imaizumi, Kohei Hayashi
Tensor Decomposition with Smoothness. ICML (2017)
Masaaki Imaizumi, Kohei Hayashi
Doubly Decomposing Nonparametric Tensor Regression. ICML (2016)
Mamoru Tanaka, Kenya Jin'no, Jun'ichi Miyata, Masaaki Imaizumi, Toshiaki Shingu, Hiroshi Inoue
Resolutionable cellular neural networks. ICNN (1997)