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07/20/2023
Synthetic Control Methods by Density Matching under Implicit Endogeneity
Synthetic control methods (SCMs) have become a crucial tool for causal i...
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07/18/2023
Uniform Confidence Band for Optimal Transport Map on One-Dimensional Data
We develop a statistical inference method for an optimal transport map b...
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07/12/2023
Distribution-on-Distribution Regression with Wasserstein Metric: Multivariate Gaussian Case
Distribution data refers to a data set where each sample is represented ...
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07/08/2023
Sup-Norm Convergence of Deep Neural Network Estimator for Nonparametric Regression by Adversarial Training
We show the sup-norm convergence of deep neural network estimators with ...
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06/19/2023
High-dimensional Contextual Bandit Problem without Sparsity
In this research, we investigate the high-dimensional linear contextual ...
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05/28/2023
Statistical Inference in High-Dimensional Generalized Linear Models with Asymmetric Link Functions
We have developed a statistical inference method applicable to a broad r...
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05/25/2023
Bayesian Analysis for Over-parameterized Linear Model without Sparsity
In high-dimensional Bayesian statistics, several methods have been devel...
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04/08/2023
Benign Overfitting of Non-Sparse High-Dimensional Linear Regression with Correlated Noise
We investigate the high-dimensional linear regression problem in situati...
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02/06/2023
Asymptotically Minimax Optimal Fixed-Budget Best Arm Identification for Expected Simple Regret Minimization
We investigate fixed-budget best arm identification (BAI) for expected s...
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01/30/2023
Adversarially Slicing Generative Networks: Discriminator Slices Feature for One-Dimensional Optimal Transport
Generative adversarial networks (GANs) learn a target probability distri...
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10/18/2022
Dimension-free Bounds for Sum of Dependent Matrices and Operators with Heavy-Tailed Distribution
We study the deviation inequality for a sum of high-dimensional random m...
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09/15/2022
Semiparametric Best Arm Identification with Contextual Information
We study best-arm identification with a fixed budget and contextual (cov...
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04/18/2022
Benign Overfitting in Time Series Linear Model with Over-Parameterization
The success of large-scale models in recent years has increased the impo...
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02/11/2022
Inference for Projection-Based Wasserstein Distances on Finite Spaces
The Wasserstein distance is a distance between two probability distribut...
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02/10/2022
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression
We study the benign overfitting theory in the prediction of the conditio...
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01/31/2022
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
This paper provides a unified perspective for the Kullback-Leibler (KL)-...
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01/12/2022
On generalization bounds for deep networks based on loss surface implicit regularization
The classical statistical learning theory says that fitting too many par...
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01/12/2022
Best Arm Identification with a Fixed Budget under a Small Gap
We consider the fixed-budget best arm identification problem in the mult...
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12/01/2021
Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression
We study a minimax risk of estimating inverse functions on a plane, whil...
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11/07/2021
Quasi-potential theory for escape problem: Quantitative sharpness effect on SGD's escape from local minima
We develop a quantitative theory on an escape problem of a stochastic gr...
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04/07/2021
Fast Convergence on Perfect Classification for Functional Data
In this study, we investigate the availability of approaching to perfect...
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02/28/2021
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks
We investigate the asymptotic risk of a general class of overparameteriz...
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02/06/2021
Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach
Dynamic graphs are rife with higher-order interactions, such as co-autho...
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02/05/2021
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency
We offer a theoretical characterization of off-policy evaluation (OPE) i...
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12/31/2020
On Gaussian Approximation for M-Estimator
This study develops a non-asymptotic Gaussian approximation theory for d...
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10/17/2019
Hypothesis Test and Confidence Analysis with Wasserstein Distance on General Dimension
We develop a general framework for statistical inference with the Wasser...
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10/15/2019
Improved Generalization Bound of Permutation Invariant Deep Neural Networks
We theoretically prove that a permutation invariant property of deep neu...
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07/04/2019
Adaptive Approximation and Estimation of Deep Neural Network to Intrinsic Dimensionality
We theoretically prove that the generalization performance of deep neura...
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01/28/2019
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
In this paper, we study random subsampling of Gaussian process regressio...
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02/13/2018
Deep Neural Networks Learn Non-Smooth Functions Effectively
We theoretically discuss why deep neural networks (DNNs) performs better...
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08/01/2017
On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm
Tensor train (TT) decomposition provides a space-efficient representatio...
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07/31/2017
Consistent Nonparametric Different-Feature Selection via the Sparsest k-Subgraph Problem
Two-sample feature selection is the problem of finding features that des...
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06/19/2015