Tomoyuki Obuchi

Toshiyuki Tanaka

IEEE Trans. Signal Process., 2024

Transfer Learning in ℓ1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis.

[BibT_eX]

[DOI]

Koki Okajima

CoRR, 2024

When resampling/reweighting improves feature learning in imbalanced classification?: A toy-model study.

[BibT_eX]

[DOI]

Toshiyuki Tanaka

CoRR, 2024

2023

On Model Selection Consistency of Lasso for High-Dimensional Ising Models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Ising Model Selection Using 𝓁1-Regularized Linear Regression.

[BibT_eX]

[DOI]

CoRR, 2021

Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sharp Asymptotics of Matrix Sketching for a Rank-One Spiked Model.

[BibT_eX]

[DOI]

Fumito Tagashira

Toshiyuki Tanaka

Proceedings of the IEEE International Symposium on Information Theory, 2021

2020

Inferring Neuronal Couplings From Spiking Data Using a Systematic Procedure With a Statistical Criterion.

[BibT_eX]

[DOI]

Neural Comput., 2020

Structure Learning in Inverse Ising Problems Using 𝓁2-Regularized Linear Estimator.

[BibT_eX]

[DOI]

CoRR, 2020

Perfect Reconstruction of Sparse Signals via Greedy Monte-Carlo Search.

[BibT_eX]

[DOI]

Kao Hayashi

CoRR, 2020

2019

Semi-Analytic Resampling in Lasso.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

Learning performance in inverse Ising problems with sparse teacher couplings.

[BibT_eX]

[DOI]

CoRR, 2019

Empirical Bayes Method for Boltzmann Machines.

[BibT_eX]

[DOI]

Muneki Yasuda

CoRR, 2019

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

[BibT_eX]

[DOI]

Ayaka Sakata

CoRR, 2019

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

[BibT_eX]

[DOI]

Ayaka Sakata

CoRR, 2019

2018

Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2018

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

[BibT_eX]

[DOI]

Yoshinori Nakanishi-Ohno

Masato Okada

CoRR, 2018

Objective and efficient inference for couplings in neuronal networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Mean-field theory of graph neural networks in graph partitioning.

[BibT_eX]

[DOI]

Tatsuro Kawamoto

Masashi Tsubaki

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2016

Sparse approximation problem: how rapid simulated annealing succeeds and fails.

[BibT_eX]

[DOI]

CoRR, 2016

Cross validation in LASSO and its acceleration.

[BibT_eX]

[DOI]

CoRR, 2016

Sampling approach to sparse approximation problem: Determining degrees of freedom by simulated annealing.

[BibT_eX]

[DOI]

Proceedings of the 24th European Signal Processing Conference, 2016

Approximate cross-validation formula for Bayesian linear regression.

[BibT_eX]

[DOI]

Makoto Uemura

Proceedings of the 54th Annual Allerton Conference on Communication, 2016

2015

Dimensionality reduction based on an overcomplete basis.

[BibT_eX]

[DOI]

Yoshinori Nakanishi-Ohno

Masato Okada

CoRR, 2015

2014

Estimation of priors in natural images.

[BibT_eX]

[DOI]

Hirokazu Koma

Muneki Yasuda

CoRR, 2014

2010

Statistical mechanical analysis of hierarchical random code ensemble in signal processing

[BibT_eX]

[DOI]