23 citations · 41 across the 10 of their papers we have counts for
8 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…
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.…
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks
Ryumei Nakada, Masaaki Imaizumi
We investigate the asymptotic risk of a general class of overparameterized likelihood models, including deep models. The recent empirical success of large-scale models has motivate…
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality
Ryumei Nakada, Masaaki Imaizumi
In this study, we prove that an intrinsic low dimensionality of covariates is the main factor that determines the performance of deep neural networks (DNNs). DNNs generally provide…
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida
In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective. Although subsampling discard…
Deep Neural Networks Learn Non-Smooth Functions Effectively
Masaaki Imaizumi, Kenji Fukumizu
We theoretically discuss why deep neural networks (DNNs) performs better than other models in some cases by investigating statistical properties of DNNs for non-smooth functions. W…