Providing a small set of promising candidates in place of a single prediction is well-suited for many open-ended classification tasks. Conformal Prediction (CP) is a technique for creating classifiers that produce a valid set of predictions that contains the true answer with arbitrarily high probability. In practice, however, standard CP can suffer from both low predictive and computational efficiency during inference---i.e., the predicted set is both unusably large, and costly to obtain. This is particularly pervasive in the considered setting, where the correct answer is not unique and the number of total possible answers is high. In this work, we develop two simple and complementary techniques for improving both types of efficiencies. First, we relax CP validity to arbitrary criterions of success---allowing our framework to make more efficient predictions while remaining "equivalently correct." Second, we amortize cost by conformalizing prediction cascades, in which we aggressively prune implausible labels early on by using progressively stronger classifiers---while still guaranteeing marginal coverage. We demonstrate the empirical effectiveness of our approach for multiple applications in natural language processing and computational chemistry for drug discovery.

该研究提出了一种新颖的conformal prediction（CP）方法，通过找到一组有前途的预测候选项而不是单个预测，以适合许多开放式分类任务，实现包含正确答案的高概率且适用于自然语言处理和药物发现等多个应用领域，同时实现降低预测集的大小和费用摊销等效果。

利用扩展准许的级联推断实现高效的符合预测