1 citations · 1 across the 5 of their papers we have counts for
5 papers
Bayesian ICA for Causal Discovery
Joe Suzuki
LiNGAM identifies causal orders by exploiting the non-Gaussianity and mutual independence of structural disturbances. Several extensions allow particular forms of latent confoundin…
Spacing Test for Fused Lasso
Rieko Tasaka, Tatsuya Kimura, Joe Suzuki
This paper studies the spacing test for the one-dimensional fused lasso. In post-selection inference for generalized lasso problems, the selective law is typically described by com…
Estimation of the Learning Coefficient Using Empirical Loss
Tatsuyoshi Takio, Joe Suzuki
The learning coefficient plays a crucial role in analyzing the performance of information criteria, such as the Widely Applicable Information Criterion (WAIC) and the Widely Applic…
An Efficient Procedure for Computing Bayesian Network Structure Learning
Hongming Huang, Joe Suzuki
We propose a globally optimal Bayesian network structure discovery algorithm based on a progressively leveled scoring approach. Bayesian network structure discovery is a fundamenta…
Post-Selection Inference for Sparse Estimation
Joe Suzuki
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 t…