
Fast Convergence on Perfect Classification for Functional Data
In this study, we investigate the availability of approaching to perfect...
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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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Understanding Higherorder Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach
Dynamic graphs are rife with higherorder interactions, such as coautho...
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Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and FirstOrder Efficiency
We offer a theoretical characterization of offpolicy evaluation (OPE) i...
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On Gaussian Approximation for MEstimator
This study develops a nonasymptotic Gaussian approximation theory for d...
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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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Improved Generalization Bound of Permutation Invariant Deep Neural Networks
We theoretically prove that a permutation invariant property of deep neu...
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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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On Random Subsampling of Gaussian Process Regression: A GraphonBased Analysis
In this paper, we study random subsampling of Gaussian process regressio...
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Deep Neural Networks Learn NonSmooth Functions Effectively
We theoretically discuss why deep neural networks (DNNs) performs better...
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On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm
Tensor train (TT) decomposition provides a spaceefficient representatio...
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Consistent Nonparametric DifferentFeature Selection via the Sparsest kSubgraph Problem
Twosample feature selection is the problem of finding features that des...
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Doubly Decomposing Nonparametric Tensor Regression
Nonparametric extension of tensor regression is proposed. Nonlinearity i...
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Masaaki Imaizumi
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