从数据视角梳理了不确定性量化的新方法。
Conformal Prediction: A Data Perspective
- 以数据为中心重构置信预测框架，适配多模态数据
- 支持结构化、非结构化及动态数据的可靠预测
- 适合处理大规模复杂数据与黑箱模型的研究者
置信预测（Conformal Prediction, CP）是一种无需分布假设的不确定性量化（UQ）框架，可为黑箱模型提供可靠的预测推断。CP 构建包含真实输出的预测集，且具有指定概率保证。然而，现代数据科学中多模态数据、日益增长的数据规模和模型复杂性，对传统 CP 方法提出挑战。这些发展催生了应对新场景的新型方法。本文从数据中心视角综述了 CP 的基础概念与最新进展，涵盖对结构化、非结构化及动态数据的应用。同时讨论了在大规模数据与模型下 CP 面临的挑战与机遇。
原文摘要 · Abstract (English)
Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework, reliably provides valid predictive inference for black-box models. CP constructs prediction sets that contain the true output with a specified probability. However, modern data science diverse modalities, along with increasing data and model complexity, challenge traditional CP methods. These developments have spurred novel approaches to address evolving scenarios. This survey reviews the foundational concepts of CP and recent advancements from a data-centric perspective, including applications to structured, unstructured, and dynamic data. We also discuss the challenges and opportunities CP faces in large-scale data and models.
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