Stable multi-instance learning visa causal inference
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Multi-instance learning (MIL) deals with tasks where each example is represented by a bag of instances. Unlike traditional supervised learning, only the bag labels are observed whereas the label for each instance in the bags is not available. Previous MIL studies typically assume that training and the test data follow the same distribution, which is often violated in real-world applications. Existing methods address distribution changes by reweighting the training bags with the density ratio between the test and the training data. However, models are frequently trained without prior knowledge of the testing distribution which renders existing methods ineffective. In this paper, we propose a novel multi-instance learning algorithm which links MIL with causal inference to achieve stable prediction without knowing the distribution of the test dataset. Experimental results show that the performance of our approach is stable to the distribution changes.
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