用贝叶斯积分方法改进置信预测，让模型不确定性更可信、可解释。
Conformal Prediction as Bayesian Quadrature
- 从贝叶斯视角重看置信预测，突破传统频率学派限制。
- 提出基于贝叶斯积分的新方法，给出可解释的损失范围保证。
- 适合关注模型可靠性与不确定性建模的研究者和工程师。
随着机器学习预测系统在高风险场景中日益普及，理解其部署后的表现至关重要。分布无关的不确定性量化技术如置信预测，可在不依赖模型细节的情况下提供损失保证。然而，这些方法基于频率学派概率，限制了其应用范围。本文从贝叶斯视角重新审视置信预测的核心机制，揭示频率学派保证的局限性。提出一种基于贝叶斯积分的实用替代方案，能提供可解释的保证，并对测试时可能遭遇的损失范围给出更丰富的表征。
原文摘要 · Abstract (English)
As machine learning-based prediction systems are increasingly used in high-stakes situations, it is important to understand how such predictive models will perform upon deployment. Distribution-free uncertainty quantification techniques such as conformal prediction provide guarantees about the loss black-box models will incur even when the details of the models are hidden. However, such methods are based on frequentist probability, which unduly limits their applicability. We revisit the central aspects of conformal prediction from a Bayesian perspective and thereby illuminate the shortcomings of frequentist guarantees. We propose a practical alternative based on Bayesian quadrature that provides interpretable guarantees and offers a richer representation of the likely range of losses to be observed at test time.
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