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 predictors. RAIL re...
Sazan Mahbub, Caleb N. Ellington, Zhi-Yuan Li et al.
· arXiv.org · 1 citation