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The Empirical Layer, a set of precomputed statistical artifacts comprising summaries, fitted feature and interaction effects, and dataset descriptions, which exposes candidate patterns for direct inspection is proposed, which raises feature-contribution accuracy from 41.9% to 68.0%.
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...
This work introduces RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem and shows that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators.
This article provides a unified view of context-adaptive inference across three traditions that are usually treated separately: explicit adaptation in statistics, rapid task-specific adaptation in meta-learning and transfer, and implicit adaptation in large foundation models via prompting, retrieval, and expert routing...
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