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cs.LG2026
Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
Yushi Hirose, Hiroo Irobe, Takafumi Kanamori
Typical semi-supervised learning (SSL) methods rely on distributional assumptions, and their performance degrades when these are violated. While PNU learning, a risk rewriting meth…
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…
cs.LG2021
Transductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embedding
Yushi Hirose, Masashi Shimbo, Taro Watanabe
For knowledge graph completion, two major types of prediction models exist: one based on graph embeddings, and the other based on relation path rule induction. They have different…