Imaizumi, Masaaki

26 publications

ICML 2025 Distillation of Discrete Diffusion Through Dimensional Correlations Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi, Hiromi Wakaki, Yuki Mitsufuji

TMLR 2025 Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution Naoki Yoshida, Shogo Nakakita, Masaaki Imaizumi

CLeaR 2025 Encode-Decoder-Based GAN for Estimating Counterfactual Outcomes Under Sequential Selection Bias and Combinatorial Explosion Yoshiyuki Norimatsu, Masaaki Imaizumi

NeurIPS 2025 Infinite-Width Limit of a Single Attention Layer: Analysis via Tensor Programs Mana Sakai, Ryo Karakida, Masaaki Imaizumi

AISTATS 2025 Learning a Single Index Model from Anisotropic Data with Vanilla Stochastic Gradient Descent Guillaume Braun, Minh Ha Quang, Masaaki Imaizumi

NeurIPS 2025 Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning Baiyuan Chen, Shinji Ito, Masaaki Imaizumi

ICMLW 2024 Automatic Domain Adaptation by Transformers in In-Context Learning Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi

NeurIPSW 2024 Distillation of Discrete Diffusion Through Dimensional Correlations Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi, Hiromi Wakaki, Yuki Mitsufuji

ICMLW 2024 Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution Naoki Yoshida, Shogo Nakakita, Masaaki Imaizumi

ICLR 2024 SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer Yuhta Takida, Masaaki Imaizumi, Takashi Shibuya, Chieh-Hsin Lai, Toshimitsu Uesaka, Naoki Murata, Yuki Mitsufuji

ICMLW 2024 Transformers as Stochastic Optimizers Ryuichiro Hataya, Masaaki Imaizumi

ICMLW 2023 Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, Toru Kitagawa

ICMLW 2023 Fixed-Budget Hypothesis Best Arm Identification: On the Information Loss in Experimental Design Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, Toru Kitagawa

NeurIPS 2023 High-Dimensional Contextual Bandit Problem Without Sparsity Junpei Komiyama, Masaaki Imaizumi

AISTATS 2023 Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics Masahiro Kato, Masaaki Imaizumi, Kentaro Minami

JMLR 2022 Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces Masaaki Imaizumi, Kenji Fukumizu

ICLR 2022 Learning Causal Models from Conditional Moment Restrictions by Importance Weighting Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Shota Yasui, Haruo Kakehi

UAI 2021 Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano

JMLR 2020 Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality Ryumei Nakada, Masaaki Imaizumi

AISTATS 2020 On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida

AISTATS 2019 Deep Neural Networks Learn Non-Smooth Functions Effectively Masaaki Imaizumi, Kenji Fukumizu

AISTATS 2018 Statistically Efficient Estimation for Non-Smooth Probability Densities Masaaki Imaizumi, Takanori Maehara, Yuichi Yoshida

IJCAI 2017 Factorized Asymptotic Bayesian Policy Search for POMDPs Masaaki Imaizumi, Ryohei Fujimaki

NeurIPS 2017 On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi

ICML 2017 Tensor Decomposition with Smoothness Masaaki Imaizumi, Kohei Hayashi

ICML 2016 Doubly Decomposing Nonparametric Tensor Regression Masaaki Imaizumi, Kohei Hayashi