distribution-free learning 1multiclass classification 1risk estimation 1semi-supervised learning 1variance reduction 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
Yushi Hirose, Hiroo Irobe, Takafumi Kanamori
The paper introduces a generalized, distribution‑free framework for semi‑supervised learning that builds unbiased risk estimators for both binary and multiclass problems, achieving…
stat.ML2026
Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence
Takafumi Kanamori, Yushi Hirose, Shohei Yamamoto
We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weig…
cs.LG2026
Mixture Proportion Estimation and Weakly-supervised Kernel Test for Conditional Independence
Yushi Hirose, Akito Narahara, Takafumi Kanamori
Mixture proportion estimation (MPE) aims to estimate class priors from unlabeled data. This task is a critical component in weakly supervised learning, such as PU learning, learnin…