Graph-Based Semi-Supervised Learning with Non-ignorable Non-response

NeurIPS 2019 Fan ZhouTengfei LiHaibo ZhouHongtu ZhuYe Jieping

Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled. When the labelling status depends on the unobserved node response, ignoring the missingness can lead to significant estimation bias and handicap the classifiers... (read more)

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