Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Suzuki, Joe"'
Autor:
Huang, Hongming, Suzuki, Joe
We propose a globally optimal Bayesian network structure discovery algorithm based on a progressively leveled scoring approach. Bayesian network structure discovery is a fundamental yet NP-hard problem in the field of probabilistic graphical models,
Externí odkaz:
http://arxiv.org/abs/2407.17072
Autor:
Liu, Lirui, Suzuki, Joe
We introduce a novel Information Criterion (IC), termed Learning under Singularity (LS), designed to enhance the functionality of the Widely Applicable Bayes Information Criterion (WBIC) and the Singular Bayesian Information Criterion (sBIC). LS is e
Externí odkaz:
http://arxiv.org/abs/2402.12762
Autor:
Suzuki, Joe, Yang, Tian-Le
LiNGAM determines the variable order from cause to effect using additive noise models, but it faces challenges with confounding. Previous methods maintained LiNGAM's fundamental structure while trying to identify and address variables affected by con
Externí odkaz:
http://arxiv.org/abs/2401.16661
Autor:
Suzuki, Joe
Publikováno v:
The Journal of the Japan Statistical Society, Volume 53, Issue 1, September 2023 (pages 139-167) (in Japanese)
When the model is not known and parameter testing or interval estimation is conducted after model selection, it is necessary to consider selective inference. This paper discusses this issue in the context of sparse estimation. Firstly, we describe se
Externí odkaz:
http://arxiv.org/abs/2310.05685
Autor:
Yang, Tian-Le, Suzuki, Joe
This study demonstrates that double descent can be mitigated by adding a dropout layer adjacent to the fully connected linear layer. The unexpected double-descent phenomenon garnered substantial attention in recent years, resulting in fluctuating pre
Externí odkaz:
http://arxiv.org/abs/2305.16179
Autor:
Shimmura, Ryosuke, Suzuki, Joe
In this paper, we propose new methods to efficiently solve convex optimization problems encountered in sparse estimation, which include a new quasi-Newton method that avoids computing the Hessian matrix and improves efficiency, and we prove its fast
Externí odkaz:
http://arxiv.org/abs/2211.16804
Autor:
Suzuki, Joe, Inaoka, Yusuke
Publikováno v:
Behaviormetrika 2021
This paper considers an extension of the linear non-Gaussian acyclic model (LiNGAM) that determines the causal order among variables from a dataset when the variables are expressed by a set of linear equations, including noise. In particular, we assu
Externí odkaz:
http://arxiv.org/abs/2108.04947
We consider learning an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction method of multipliers (ADMM) is the main approach taken concerning for join
Externí odkaz:
http://arxiv.org/abs/2107.07799
Autor:
Shimmura, Ryosuke, Suzuki, Joe
In sparse estimation, such as fused lasso and convex clustering, we apply either the proximal gradient method or the alternating direction method of multipliers (ADMM) to solve the problem. It takes time to include matrix division in the former case,
Externí odkaz:
http://arxiv.org/abs/2104.10911
Autor:
Shimmura, Ryosuke1 (AUTHOR) prof.joe.suzuki@gmail.com, Suzuki, Joe1 (AUTHOR)
Publikováno v:
Entropy. Jan2024, Vol. 26 Issue 1, p44. 13p.