13 citations · 39 across the 10 of their papers we have counts for
10 papers
Dynamical Regimes of Discrete Diffusion Models
Tomoei Takahashi, Takashi Takahashi, Yoshiyuki Kabashima
Diffusion models generate high-dimensional data such as images by learning a process that gradually removes noise from corrupted data. Recent studies have shown that the backward d…
Compressed Sensing Radar Detectors based on Weighted LASSO
Siqi Na, Yoshiyuki Kabashima, Takashi Takahashi +3
The compressed sensing (CS) model can represent the signal recovery process of a large number of radar systems. The detection problem of such radar systems has been studied in many…
Average case analysis of Lasso under ultra-sparse conditions
Koki Okajima, Xiangming Meng, Takashi Takahashi +1
We analyze the performance of the least absolute shrinkage and selection operator (Lasso) for the linear model when the number of regressors grows larger keeping the true suppo…
Role of Bootstrap Averaging in Generalized Approximate Message Passing
Takashi Takahashi
Generalized approximate message passing (GAMP) is a computationally efficient algorithm for estimating an unknown signal from a random linear measurement $y= X…
Compressed sensing radar detectors under the row-orthogonal design model: a statistical mechanics perspective
Siqi Na, Tianyao Huang, Yimin Liu +3
Compressed sensing (CS) model of complex-valued data can represent the signal recovery process of a large amount types of radar systems, especially when the measurement matrix is r…
Statistical mechanics analysis of general multi-dimensional knapsack problems
Yuta Nakamura, Takashi Takahashi, Yoshiyuki Kabashima
Knapsack problem (KP) is a representative combinatorial optimization problem that aims to maximize the total profit by selecting a subset of items under given constraints on the to…
Semi-analytic approximate stability selection for correlated data in generalized linear models
Takashi Takahashi, Yoshiyuki Kabashima
We consider the variable selection problem of generalized linear models (GLMs). Stability selection (SS) is a promising method proposed for solving this problem. Although SS provid…
Macroscopic Analysis of Vector Approximate Message Passing in a Model Mismatch Setting
Takashi Takahashi, Yoshiyuki Kabashima
Vector approximate message passing (VAMP) is an efficient approximate inference algorithm used for generalized linear models. Although VAMP exhibits excellent performance, particul…
Replicated Vector Approximate Message Passing For Resampling Problem
Takashi Takahashi, Yoshiyuki Kabashima
Resampling techniques are widely used in statistical inference and ensemble learning, in which estimators' statistical properties are essential. However, existing methods are compu…
A statistical mechanics approach to de-biasing and uncertainty estimation in LASSO for random measurements
Takashi Takahashi, Yoshiyuki Kabashima
In high-dimensional statistical inference in which the number of parameters to be estimated is larger than that of the holding data, regularized linear estimation techniques are wi…