Rethinking Learning from Label Proportions via Moment Matching

The paper introduces LLP-MM, a method for learning from label proportions that uses moment matching based on the Poisson multinomial distribution. It addresses the limitation of prior approaches that assume i.i.d. instance-label pairs, showing that these methods underperform when this assumption is violated. Experiments on benchmark datasets with various bag construction strategies demonstrate that LLP-MM is both effective and computationally efficient.

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