基于扩展接纳级联推理的高效共形预测
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
摘要：
在本文中，我们提出了一种新的方法，共形预测（CP），其中我们的目标是确定一组有前途的预测候选人-在一个单一的预测。该集合保证包含具有高概率的正确答案，并且非常适合于许多开放式分类任务。在标准的CP范例中，预测集通常会大得无法使用，并且获得成本也很高。这在正确答案不是唯一的，并且可能的答案总数很高的情况下尤其普遍。我们首先扩展CP正确性标准，以允许额外的，推断的“可接受”的答案，这可以大大减少预测集的大小，同时仍然提供有效的性能保证。其次，我们通过整合预测级联来摊销成本，在这种情况下，我们通过使用逐渐增强的分类器来积极地在早期修剪不可信的标签，同时仍然提供有效的性能保证。我们证明了我们的方法在自然语言处理和计算化学药物发现的多个应用程序的经验有效性。
英文摘要：
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.
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