Tomoyuki Obuchi | DeepAI

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Featured Co-authors

Yoshiyuki Kabashima
22 publications
Muneki Yasuda
21 publications
Xiangming Meng
19 publications
Masato Okada
15 publications
Ayaka Sakata
9 publications
Yingying Xu
8 publications
Tatsuro Kawamoto
6 publications
Alia Abbara
5 publications
Masashi Tsubaki
4 publications
Yoshinori Nakanishi-Ohno
2 publications
Makoto Uemura
2 publications

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 06/14/2019

Empirical Bayes Method for Boltzmann Machines

In this study, we consider an empirical Bayes method for Boltzmann machi...

0 Muneki Yasuda, et al. ∙

∙ 02/27/2019

Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration

We investigate the signal reconstruction performance of sparse linear re...

0 Tomoyuki Obuchi, et al. ∙

∙ 02/20/2019

Perfect reconstruction of sparse signals with piecewise continuous nonconvex penalties and nonconvexity control

We consider compressed sensing formulated as a minimization problem of n...

0 Ayaka Sakata, et al. ∙

∙ 10/29/2018

Mean-field theory of graph neural networks in graph partitioning

A theoretical performance analysis of the graph neural network (GNN) is ...

0 Tatsuro Kawamoto, 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. ∙

∙ 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. ∙

∙ 12/16/2014

Boltzmann-Machine Learning of Prior Distributions of Binarized Natural Images

Prior distributions of binarized natural images are learned by using a B...

0 Tomoyuki Obuchi, et al. ∙

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