Weak-to-Strong Learning in Decision Making
Many operational decisions rely on predictive models that estimate uncertain outcomes conditional on observable contexts. Training such models, however, often faces a fundamental data asymmetry: labeled outcomes are scarce or costly to obtain, while contextual covariates are abundant. Motivated by this data asymmetry, we develop a decision-aware weak-to-strong (W2S) framework that leverages both labeled and unlabeled data to improve contextual stochastic optimization. Specifically, we first train a weak model using limited labeled data and then use it to generate predicted outcome distributions on unlabeled contexts. These distributions provide soft supervision for training a strong model...
| Authors | Jingwei Ji, Renyuan Xu |
|---|---|
| Primary category | cs.LG |
| Published | 20 July 2026 |
| Last updated at source | 25 August 2026 |
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