We develop a novel approach to conformal prediction when the target task has limited data available for training. Conformal prediction identifies a small set of promising output candidates in place of a single prediction, with guarantees that the set contains the correct answer with high probability. When training data is limited, however, the predicted set can easily become unusably large. In this work, we obtain substantially tighter prediction sets while maintaining desirable marginal guarantees by casting conformal prediction as a meta-learning paradigm over exchangeable collections of auxiliary tasks. Our conformalization algorithm is simple, fast, and agnostic to the choice of underlying model, learning algorithm, or dataset. We demonstrate the effectiveness of this approach across a number of few-shot classification and regression tasks in natural language processing, computer vision, and computational chemistry for drug discovery.

翻译：当目标任务为培训提供的数据有限时,我们开发了一种符合预测的新办法,在目标任务为培训提供的数据有限时,我们开发了一种符合预测的新办法; 非正式预测确定了一组有希望的产出候选人,以取代单一的预测,同时保证这套预测包含的正确答案概率很高; 但是,当培训数据有限时,预测的数据集很容易变得不可避免大。 在这项工作中,我们获得的预测数据集大大收紧,同时保持适当的边际保障,方法是将符合预测作为可交换的辅助任务收集的元学模式。 我们的符合算法简单、快速和不可知性,以选择基本模型、学习算法或数据集。 我们展示了这种方法在天然语言处理、计算机视觉和药物发现计算化学方面的若干微小的分类和回归任务中的有效性。