11 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…
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…
Minimax Rates of Estimation for Optimal Transport Map between Infinite-Dimensional Spaces
Donlapark Ponnoprat, Masaaki Imaizumi
We investigate the estimation of an optimal transport map between probability measures on an infinite-dimensional space and reveal its minimax optimal rate. Optimal transport theor…
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…
Universality of estimators for high-dimensional linear models with block dependency
Toshiki Tsuda, Masaaki Imaizumi
We study the universality property of estimators for high-dimensional linear models, which implies that the distribution of estimators is independent of whether the covariates foll…