56 citations · 205 across the 38 of their papers we have counts for
39 papers
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…
Best Arm Identification with Contextual Information under a Small Gap
Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara +1
We study the best-arm identification (BAI) problem with a fixed budget and contextual (covariate) information. In each round of an adaptive experiment, after observing contextual i…
Benign Overfitting in Time Series Linear Models with Over-Parameterization
Shogo Nakakita, Masaaki Imaizumi
The success of large-scale models in recent years has increased the importance of statistical models with numerous parameters. Several studies have analyzed over-parameterized line…
Inference for Projection-Based Wasserstein Distances on Finite Spaces
Ryo Okano, Masaaki Imaizumi
The Wasserstein distance is a distance between two probability distributions and has recently gained increasing popularity in statistics and machine learning, owing to its attracti…
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression
Masahiro Kato, Masaaki Imaizumi
We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models. As the development of machine learning f…
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
Masahiro Kato, Masaaki Imaizumi, Kentaro Minami
This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ra…
On generalization bounds for deep networks based on loss surface implicit regularization
Masaaki Imaizumi, Johannes Schmidt-Hieber
The classical statistical learning theory implies that fitting too many parameters leads to overfitting and poor performance. That modern deep neural networks generalize well despi…
Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap
Masahiro Kato, Kaito Ariu, Masaaki Imaizumi +2
We consider fixed-budget best-arm identification in two-armed Gaussian bandit problems. One of the longstanding open questions is the existence of an optimal strategy under which t…
Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression
Akifumi Okuno, Masaaki Imaizumi
We study a minimax risk of estimating inverse functions on a plane, while keeping an estimator is also invertible. Learning invertibility from data and exploiting an invertible est…
Exponential escape efficiency of SGD from sharp minima in non-stationary regime
Hikaru Ibayashi, Masaaki Imaizumi
We show that stochastic gradient descent (SGD) escapes from sharp minima exponentially fast even before SGD reaches stationary distribution. SGD has been a de-facto standard traini…
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn +2
We consider learning causal relationships under conditional moment restrictions. Unlike causal inference under unconditional moment restrictions, conditional moment restrictions po…
Minimum sharpness: Scale-invariant parameter-robustness of neural networks
Hikaru Ibayashi, Takuo Hamaguchi, Masaaki Imaizumi
Toward achieving robust and defensive neural networks, the robustness against the weight parameters perturbations, i.e., sharpness, attracts attention in recent years (Sun et al.,…
Instrument Space Selection for Kernel Maximum Moment Restriction
Rui Zhang, Krikamol Muandet, Bernhard Schölkopf +1
Kernel maximum moment restriction (KMMR) recently emerges as a popular framework for instrumental variable (IV) based conditional moment restriction (CMR) models with important app…
Fast Convergence on Perfect Classification for Functional Data
Tomoya Wakayama, Masaaki Imaizumi
We investigate the availability of approaching perfect classification on functional data with finite samples. The seminal work (Delaigle and Hall (2012)) showed that perfect classi…
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…
Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach
Manohar Kaul, Masaaki Imaizumi
Dynamic graphs are rife with higher-order interactions, such as co-authorship relationships and protein-protein interactions in biological networks, that naturally arise between mo…