Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization

2023/02/17 by Shinnosuke Matsuo, Matsuo, Shinnosuke, Ryoma Bise +5 · 1 citation
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Text and Document Classification Technologies

paper · pdf · doi:10.48550/arxiv.2302.08947

openalex publication_date 2023/02/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags. We propose a novel LLP method based on an online pseudo-labeling method with regret minimization. As opposed to the previous LLP methods, the proposed method effectively works even if the bag sizes are large. We demonstrate the effectiveness of the proposed method using some benchmark datasets.

Cited by

• Weakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-Labeling

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