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In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
This paper develops a finite-sample statistical theory for in-context learning (ICL), analyzed within a meta-learning framework that accommodates mixtures of diverse task types. We introduce a principled risk decomposition that separates the total ICL risk into two orthogonal components: Bayes Gap a
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