1 citations · 1 across the 11 of their papers we have counts for
14 papers
Finite-Sample Inference for Sparsely Permuted Linear Regression
Hirofumi Ota, Masaaki Imaizumi
We study a linear observation model with an unknown permutation called \textit{permuted/shuffled linear regression}, where responses and covariates are mismatched and the permutati…
Zero Generalization Error Theorem for Random Interpolators via Algebraic Geometry
Naoki Yoshida, Isao Ishikawa, Masaaki Imaizumi
We theoretically demonstrate that the generalization error of interpolators for machine learning models under teacher-student settings becomes 0 once the number of training samples…
Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning
Baiyuan Chen, Shinji Ito, Masaaki Imaizumi
Transformers have demonstrated exceptional performance across a wide range of domains. While their ability to perform reinforcement learning in-context has been established both th…
Infinite-Width Limit of a Single Attention Layer: Analysis via Tensor Programs
Mana Sakai, Ryo Karakida, Masaaki Imaizumi
In modern theoretical analyses of neural networks, the infinite-width limit is often invoked to justify Gaussian approximations of neuron preactivations (e.g., via neural network G…
High-dimensional Nonparametric Contextual Bandit Problem
Shogo Iwazaki, Junpei Komiyama, Masaaki Imaizumi
We consider the kernelized contextual bandit problem with a large feature space. This problem involves arms, and the goal of the forecaster is to maximize the cumulative reward…
Precise gradient descent training dynamics for finite-width multi-layer neural networks
Qiyang Han, Masaaki Imaizumi
In this paper, we provide the first precise distributional characterization of gradient descent iterates for general multi-layer neural networks under the canonical single-index re…