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Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific
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