120 citations · 177 across the 7 of their papers we have counts for
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cs.LG2021
Two-stage Training for Learning from Label Proportions
Jiabin Liu, Bo Wang, Xin Shen +2
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utiliz…
cs.LG2019★ 120 cited
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification
Guoqiang Wu, Ruobing Zheng, Yingjie Tian +1
Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims…
cs.LG2019
Learning from Label Proportions with Generative Adversarial Networks
Jiabin Liu, Bo Wang, Zhiquan Qi +2
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level propo…