[2508.05108] Learning from Similarity-Confidence and Confidence-Difference

Computer Science > Machine Learning arXiv:2508.05108 (cs) [Submitted on 7 Aug 2025 (v1), last revised 23 Mar 2026 (this version, v3)] Title:Learning from Similarity-Confidence and Confidence-Difference Authors:Tomoya Tate, Kosuke Sugiyama, Masato Uchida View a PDF of the paper titled Learning from Similarity-Confidence and Confidence-Difference, by Tomoya Tate and 2 other authors View PDF Abstract:In practical machine learning applications, it is often challenging to assign accurate labels to data, and increasing the number of labeled instances is often limited. In such cases, Weakly Supervised Learning (WSL), which enables training with incomplete or imprecise supervision, provides a practical and effective solution. However, most existing WSL methods focus on leveraging a single type of weak supervision. In this paper, we propose a novel WSL framework that leverages complementary weak supervision signals from multiple relational perspectives, which can be especially valuable when labeled data is limited. Specifically, we introduce SconfConfDiff Classification, a method that integrates two distinct forms of weaklabels: similarity-confidence and confidence-difference, which are assigned to unlabeled data pairs. To implement this method, we derive two types of unbiased risk estimators for classification: one based on a convex combination of existing estimators, and another newly designed by modeling the interaction between two weak labels. We prove that both estimators achi...