Weak-to-Strong Learning Framework Improves Decision Making with Scarce Labels
Researchers introduce a decision-aware weak-to-strong (W2S) learning framework that addresses the challenge of scarce labeled data in operational decision-making. The approach first trains a weak model on limited labeled data, then uses its predictions to generate soft labels for abundant unlabeled contexts, enabling the training of a stronger model. Theoretical analysis provides bounds on decision risk, and experiments on synthetic and real-world tasks support the method's effectiveness.
Why it matters: This work offers a principled way to leverage abundant unlabeled data to improve decision-making when labeled data are limited, addressing a common challenge in real-world applications.
Full story at: arXiv Machine Learning ↗