Yoshiyuki Kabashima | DeepAI

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Florent Krzakala
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Jukka Corander
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Xiangming Meng
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Masato Okada
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Tomoyuki Obuchi
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Marc Mézard
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Johan Pensar
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Ayaka Sakata
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Yingying Xu
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Takashi Takahashi
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∙ 02/25/2023

Average case analysis of Lasso under ultra-sparse conditions

We analyze the performance of the least absolute shrinkage and selection...

0 Koki Okajima, et al. ∙

∙ 02/02/2023

QCM-SGM+: Improved Quantized Compressed Sensing With Score-Based Generative Models for General Sensing Matrices

In realistic compressed sensing (CS) scenarios, the obtained measurement...

0 Xiangming Meng, et al. ∙

∙ 11/20/2022

Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems

We consider the ubiquitous linear inverse problems with additive Gaussia...

0 Xiangming Meng, et al. ∙

∙ 11/02/2022

Quantized Compressed Sensing with Score-Based Generative Models

We consider the general problem of recovering a high-dimensional signal ...

0 Xiangming Meng, et al. ∙

∙ 10/21/2021

Decision Theoretic Cutoff and ROC Analysis for Bayesian Optimal Group Testing

We study the inference problem in the group testing to identify defectiv...

0 Ayaka Sakata, et al. ∙

∙ 10/16/2021

On Model Selection Consistency of Lasso for High-Dimensional Ising Models on Tree-like Graphs

We consider the problem of high-dimensional Ising model selection using ...

0 Xiangming Meng, et al. ∙

∙ 05/01/2021

Matrix completion based on Gaussian belief propagation

We develop a message-passing algorithm for noisy matrix completion probl...

0 Koki Okajima, et al. ∙

∙ 02/08/2021

Ising Model Selection Using ℓ_1-Regularized Linear Regression

We theoretically investigate the performance of ℓ_1-regularized linear r...

0 Xiangming Meng, et al. ∙

∙ 08/19/2020

Structure Learning in Inverse Ising Problems Using ℓ_2-Regularized Linear Estimator

Inferring interaction parameters from observed data is a ubiquitous requ...

0 Xiangming Meng, et al. ∙

∙ 08/07/2020

Perfect Reconstruction of Sparse Signals via Greedy Monte-Carlo Search

We propose a Monte-Carlo-based method for reconstructing sparse signals ...

0 Kao Hayash, et al. ∙

∙ 03/19/2020

Semi-analytic approximate stability selection for correlated data in generalized linear models

We consider the variable selection problem of generalized linear models ...

0 Takashi Takahashi, et al. ∙

∙ 01/09/2020

Macroscopic Analysis of Vector Approximate Message Passing in a Model Mismatch Setting

Vector approximate message passing (VAMP) is an efficient approximate in...

0 Takashi Takahashi, et al. ∙

∙ 12/25/2019

Learning performance in inverse Ising problems with sparse teacher couplings

We investigate the learning performance of the pseudolikelihood maximiza...

0 Alia Abbara, et al. ∙

∙ 06/29/2019

Approximate matrix completion based on cavity method

In order to solve large matrix completion problems with practical comput...

0 Chihiro Noguchi, et al. ∙

∙ 05/23/2019

Replicated Vector Approximate Message Passing For Resampling Problem

Resampling techniques are widely used in statistical inference and ensem...

0 Takashi Takahashi, et al. ∙

∙ 01/14/2019

High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study

Learning the undirected graph structure of a Markov network from data is...

0 Johan Pensar, et al. ∙

∙ 05/29/2018

Statistical mechanical analysis of sparse linear regression as a variable selection problem

An algorithmic limit of compressed sensing or related variable-selection...

2 Tomoyuki Obuchi, et al. ∙

∙ 03/27/2018

A statistical mechanics approach to de-biasing and uncertainty estimation in LASSO for random measurements

In high-dimensional statistical inference in which the number of paramet...

0 Takashi Takahashi, et al. ∙

∙ 02/28/2018

Semi-Analytic Resampling in Lasso

An approximate method for conducting resampling in Lasso, the ℓ_1 penali...

0 Tomoyuki Obuchi, et al. ∙

∙ 11/15/2017

Accelerating Cross-Validation in Multinomial Logistic Regression with ℓ_1-Regularization

We develop an approximate formula for evaluating a cross-validation esti...

0 Tomoyuki Obuchi, et al. ∙

∙ 10/25/2016

Approximate cross-validation formula for Bayesian linear regression

Cross-validation (CV) is a technique for evaluating the ability of stati...

0 Yoshiyuki Kabashima, et al. ∙

∙ 02/06/2014

Phase transitions and sample complexity in Bayes-optimal matrix factorization

We analyse the matrix factorization problem. Given a noisy measurement o...

0 Yoshiyuki Kabashima, et al. ∙

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