This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of tasks, including hypothesis testing and providing uncertainty quantification guarantees for machine learning systems. Much of the current interest in conformal prediction is due to its ability to integrate into complex machine learning workflows, solving the problem of forming prediction sets without any assumptions on the form of the data generating distribution. Since contemporary machine learning algorithms have generally proven difficult to analyze directly, conformal prediction's main appeal is its ability to provide formal, finite-sample guarantees when paired with such methods. The goal of this book is to teach the reader about the fundamental technical arguments that arise when researching conformal prediction and related questions in distribution-free inference. Many of these proof strategies, especially the more recent ones, are scattered among research papers, making it difficult for researchers to understand where to look, which results are important, and how exactly the proofs work. We hope to bridge this gap by curating what we believe to be some of the most important results in the literature and presenting their proofs in a unified language, with illustrations, and with an eye towards pedagogy.

翻译：本书探讨共形预测及其基于置换检验与可交换性的相关推断技术。这些技术在多种任务中具有重要价值，包括假设检验和为机器学习系统提供不确定性量化保证。当前对共形预测的关注主要源于其能够融入复杂的机器学习工作流，在无需对数据生成分布形式作任何假设的前提下构建预测集。由于当代机器学习算法通常难以直接进行理论分析，共形预测的核心优势在于能为这类方法提供严格的有限样本保证。本书旨在向读者阐释研究共形预测及无分布推断相关问题时涉及的基础理论论证。许多证明策略（尤其是较新的成果）分散在各研究论文中，导致研究者难以明确研究方向、识别重要结论及理解证明细节。我们希望通过梳理文献中的重要成果，用统一规范的表述、图示说明和教学视角呈现其证明过程，从而弥合这一认知鸿沟。