3 papers
cs.IT2026
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…
math.ST2025
Spacing Test for Fused Lasso
Rieko Tasaka, Tatsuya Kimura, Joe Suzuki
Detecting changepoints in a one-dimensional signal is a classical yet fundamental problem. The fused lasso provides an elegant convex formulation that produces a stepwise estimate…
stat.ML2025
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…