In this paper, we present a novel approach for conformal prediction (CP), in
which we aim to identify a set of promising prediction candidates -- in place
of a single prediction. This set is guaranteed to contain a correct answer with
high probability, and is well-suited for many open-ended classification tasks.
In the standard CP paradigm, the predicted set can often be unusably large and
also costly to obtain. This is particularly pervasive in settings where the
correct answer is not unique, and the number of total possible answers is high.
We first expand the CP correctness criterion to allow for additional, inferred
"admissible" answers, which can substantially reduce the size of the predicted
set while still providing valid performance guarantees. Second, we amortize
costs by conformalizing prediction cascades, in which we aggressively prune
implausible labels early on by using progressively stronger classifiers --
again, while still providing valid performance guarantees. 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。