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Tomoyuki Obuchi
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Journal Articles
- 2024
- [j4]Kyoya Uemura
, Tomoyuki Obuchi
Sparse Modeling for Spectrometer Based on Band Measurement. IEEE Trans. Signal Process. 72: 1724-1738 (2024) - 2020
- [j3]Yu Terada
Inferring Neuronal Couplings From Spiking Data Using a Systematic Procedure With a Statistical Criterion. Neural Comput. 32(11): 2187-2211 (2020) - 2019
- [j2]Tomoyuki Obuchi, Yoshiyuki Kabashima:
Semi-Analytic Resampling in Lasso. J. Mach. Learn. Res. 20: 70:1-70:33 (2019) - 2018
- [j1]Tomoyuki Obuchi, Yoshiyuki Kabashima:
Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization. J. Mach. Learn. Res. 19: 52:1-52:30 (2018)
Conference and Workshop Papers
- 2023
- [c7]Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima:
On Model Selection Consistency of Lasso for High-Dimensional Ising Models. AISTATS 2023: 6783-6805 - 2021
- [c6]Fumito Tagashira, Tomoyuki Obuchi, Toshiyuki Tanaka:
Sharp Asymptotics of Matrix Sketching for a Rank-One Spiked Model. ISIT 2021: 250-255 - [c5]Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima:
Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis. NeurIPS 2021: 6290-6303 - 2018
- [c4]Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi:
Mean-field theory of graph neural networks in graph partitioning. NeurIPS 2018: 4366-4376 - [c3]Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima:
Objective and efficient inference for couplings in neuronal networks. NeurIPS 2018: 4976-4985 - 2016
- [c2]Yoshiyuki Kabashima, Tomoyuki Obuchi, Makoto Uemura
Approximate cross-validation formula for Bayesian linear regression. Allerton 2016: 596-600 - [c1]Tomoyuki Obuchi, Yoshiyuki Kabashima:
Sampling approach to sparse approximation problem: Determining degrees of freedom by simulated annealing. EUSIPCO 2016: 1247-1251
Informal and Other Publications
- 2024
- [i19]Tomoyuki Obuchi, Toshiyuki Tanaka:
When resampling/reweighting improves feature learning in imbalanced classification?: A toy-model study. CoRR abs/2409.05598 (2024) - [i18]Koki Okajima, Tomoyuki Obuchi:
Transfer Learning in ℓ1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis. CoRR abs/2409.17704 (2024) - 2021
- [i17]Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima:
Ising Model Selection Using 𝓁1-Regularized Linear Regression. CoRR abs/2102.03988 (2021) - [i16]Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima:
On Model Selection Consistency of Lasso for High-Dimensional Ising Models on Tree-like Graphs. CoRR abs/2110.08500 (2021) - 2020
- [i15]Kao Hayashi, Tomoyuki Obuchi, Yoshiyuki Kabashima:
Perfect Reconstruction of Sparse Signals via Greedy Monte-Carlo Search. CoRR abs/2008.03175 (2020) - [i14]Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima:
Structure Learning in Inverse Ising Problems Using 𝓁2-Regularized Linear Estimator. CoRR abs/2008.08342 (2020) - 2019
- [i13]Ayaka Sakata, Tomoyuki Obuchi:
Perfect reconstruction of sparse signals with piecewise continuous nonconvex penalties and nonconvexity control. CoRR abs/1902.07436 (2019) - [i12]Tomoyuki Obuchi, Ayaka Sakata:
Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration. CoRR abs/1902.10375 (2019) - [i11]Muneki Yasuda, Tomoyuki Obuchi:
Empirical Bayes Method for Boltzmann Machines. CoRR abs/1906.06002 (2019) - [i10]Alia Abbara, Yoshiyuki Kabashima, Tomoyuki Obuchi, Yingying Xu:
Learning performance in inverse Ising problems with sparse teacher couplings. CoRR abs/1912.11591 (2019) - 2018
- [i9]Tomoyuki Obuchi, Yoshinori Nakanishi-Ohno, Masato Okada, Yoshiyuki Kabashima:
Statistical mechanical analysis of sparse linear regression as a variable selection problem. CoRR abs/1805.11259 (2018) - [i8]Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi:
Mean-field theory of graph neural networks in graph partitioning. CoRR abs/1810.11908 (2018) - 2016
- [i7]Tomoyuki Obuchi, Yoshiyuki Kabashima:
Cross validation in LASSO and its acceleration. CoRR abs/1601.00881 (2016) - [i6]Tomoyuki Obuchi, Yoshiyuki Kabashima:
Sparse approximation problem: how rapid simulated annealing succeeds and fails. CoRR abs/1601.01074 (2016) - [i5]Tomoyuki Obuchi, Yoshiyuki Kabashima:
Sampling approach to sparse approximation problem: determining degrees of freedom by simulated annealing. CoRR abs/1603.01399 (2016) - [i4]Yoshiyuki Kabashima, Tomoyuki Obuchi, Makoto Uemura:
Approximate cross-validation formula for Bayesian linear regression. CoRR abs/1610.07733 (2016) - 2015
- [i3]Yoshinori Nakanishi-Ohno, Tomoyuki Obuchi, Masato Okada, Yoshiyuki Kabashima:
Dimensionality reduction based on an overcomplete basis. CoRR abs/1510.02189 (2015) - 2014
- [i2]Tomoyuki Obuchi, Hirokazu Koma, Muneki Yasuda:
Estimation of priors in natural images. CoRR abs/1412.7012 (2014) - 2010
- [i1]Tomoyuki Obuchi, Kazutaka Takahashi, Koujin Takeda:
Statistical mechanical analysis of hierarchical random code ensemble in signal processing. CoRR abs/1011.3722 (2010)
Coauthor Index
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