56 citations · 205 across the 38 of their papers we have counts for
9 papers · 1 filter
Sup-Norm Convergence of Deep Neural Network Estimator for Nonparametric Regression by Adversarial Training
Masaaki Imaizumi
We show the sup-norm convergence of deep neural network estimators with a novel adversarial training scheme. For the nonparametric regression problem, it has been shown that an est…
Asymptotically Unbiased Synthetic Control Methods by Moment Matching
Masahiro Kato, Akari Ohda
Synthetic Control Methods (SCMs) have become a fundamental tool for comparative case studies. The core idea behind SCMs is to estimate treatment effects by predicting counterfactua…
Uniform Confidence Band for Optimal Transport Map on One-Dimensional Data
Donlapark Ponnoprat, Ryo Okano, Masaaki Imaizumi
We develop a statistical inference method for an optimal transport map between distributions on real numbers with uniform confidence bands. The concept of optimal transport (OT) is…
Distribution-on-Distribution Regression with Wasserstein Metric: Multivariate Gaussian Case
Ryo Okano, Masaaki Imaizumi
Distribution data refers to a data set where each sample is represented as a probability distribution, a subject area receiving burgeoning interest in the field of statistics. Alth…
High-dimensional Contextual Bandit Problem without Sparsity
Junpei Komiyama, Masaaki Imaizumi
In this research, we investigate the high-dimensional linear contextual bandit problem where the number of features is greater than the budget , or it may even be infinite.…
Moment-Based Adjustments of Statistical Inference in High-Dimensional Generalized Linear Models
Kazuma Sawaya, Yoshimasa Uematsu, Masaaki Imaizumi
We develop a statistical inference method for generalized linear models (GLMs) in high-dimensional settings, where the number of unknown coefficients is of the same order as th…
Benign Overfitting of Non-Sparse High-Dimensional Linear Regression with Correlated Noise
Toshiki Tsuda, Masaaki Imaizumi
We investigate the high-dimensional linear regression problem in the presence of noise correlated with Gaussian covariates. This correlation, known as endogeneity in regression mod…
Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds
Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara +1
We investigate the problem of fixed-budget best arm identification (BAI) for minimizing expected simple regret. In an adaptive experiment, a decision maker draws one of multiple tr…
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
Yuhta Takida, Masaaki Imaizumi, Takashi Shibuya +4
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the quest…