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Enhancing semi-supervised learning via self-interested coalitional learning

Semi-supervised learning holds great promise for many real-world applications, due to its ability to leverage both unlabeled and expensive labeled data. However, most semi-supervised learning algorithms still heavily rely on the limited labeled data to infer and utilize the hidden information from unlabeled data. We note that any semi-supervised learning task under the self-training paradigm also hides an auxiliary task of discriminating label observability. Jointly solving these two tasks allows full utilization of information from both labeled and unlabeled data, thus alleviating the problem of over-reliance on labeled data. This naturally leads to a new learning framework, which we call Self-interested Coalitional Learning (SCL). The key idea of SCL is to construct a semi-cooperative ``game”, which forges cooperation between a main self-interested semi-supervised learning task and a companion task that infers label observability to facilitate main task training. We show with theoretical deduction its connection to loss reweighting on noisy labels. Through comprehensive evaluation on both classification and regression tasks, we show that SCL can consistently enhance the performance of semi-supervised learning algorithms.

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