A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
共形预测与无分布不确定性量化入门AI 解读 ↓
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共形预测与无分布不确定性量化入门
- 研究问题
- 如何为任意预训练黑箱模型构造具有明确覆盖概率的预测集合或区间，并理解这些无分布不确定性保证的适用范围？
- 方法与思路
- 作者以自包含教程介绍共形预测的实用理论和示例，讨论分布变化、时间序列等扩展，并配套 Python 代码与真实数据笔记本。
- 材料陈述的贡献
- 摘要称教程系统解释共形预测及相关不确定性量化方法，介绍无需指定分布形式或模型形式的非渐近覆盖保证及复杂任务扩展。
- 阅读关注点
- 应核对覆盖保证所需的数据条件、边际与条件覆盖的区别、校准集构造，以及分布变化和时间依赖场景中保证如何调整。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未逐项说明有效性条件，不能将无分布保证理解为允许任意依赖或分布变化。
原文摘要
Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models. Critically, the sets are valid in a distribution-free sense: they possess explicit, non-asymptotic guarantees even without distributional assumptions or model assumptions. One can use conformal prediction with any pre-trained model, such as a neural network, to produce sets that are guaranteed to contain the ground truth with a user-specified probability, such as 90%. It is easy-to-understand, easy-to-use, and general, applying naturally to problems arising in the fields of computer vision, natural language processing, deep reinforcement learning, and so on. This hands-on introduction is aimed to provide the reader a working understanding of conformal prediction and related distribution-free uncertainty quantification techniques with one self-contained document. We lead the reader through practical theory for and examples of conformal prediction and describe its extensions to complex machine learning tasks involving structured outputs, distribution shift, time-series, outliers, models that abstain, and more. Throughout, there are many explanatory illustrations, examples, and code samples in Python. With each code sample comes a Jupyter notebook implementing the method on a real-data example; the notebooks can be accessed and easily run using our codebase.