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.
Theoretical Foundation of Co-Training and Disagreement-Based Algorithms
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abstract
Disagreement-based approaches generate multiple classifiers and exploit the disagreement among them with unlabeled data to improve learning performance. Co-training is a representative paradigm of them, which trains two classifiers separately on two sufficient and redundant views; while for the applications where there is only one view, several successful variants of co-training with two different classifiers on single-view data instead of two views have been proposed. For these disagreement-based approaches, there are several important issues which still are unsolved, in this article we present theoretical analyses to address these issues, which provides a theoretical foundation of co-training and disagreement-based approaches.
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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.