For convex and approximately convex model classes, the loss gain in weak-to-strong learning is at least the KL misfit between strong and weak models, plus an error term that vanishes as k grows.
Combining labeled and unlabeled data with co-training
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Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss
For convex and approximately convex model classes, the loss gain in weak-to-strong learning is at least the KL misfit between strong and weak models, plus an error term that vanishes as k grows.