教人看懂机器学习为何突然失效，给出实用排查方法。
A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practice
- 区分失效为可靠性不足或鲁棒性差，从原理上定位问题
- 梳理关键理论概念，提供可落地的检测与分析技术
- 结合真实场景案例，适合部署阶段的工程师参考
机器学习模型意外失效是其广泛应用的主要障碍。本文旨在为实践者提供一份指南，帮助理解模型失效的原因，并掌握相应的分析工具。我们提出将失效分为可靠性不足与鲁棒性差两类，这一区分使失效原因能从基础原理出发进行形式化定义，并与工程概念和实际部署场景相联系。文中系统梳理了可靠性与鲁棒性的核心理论概念，总结了当前可用的实用技术，用于评估模型的可靠性与鲁棒性，并通过实例展示这些方法在真实场景中的应用价值。
原文摘要 · Abstract (English)
One of the main barriers to adoption of Machine Learning (ML) is that ML models can fail unexpectedly. In this work, we aim to provide practitioners a guide to better understand why ML models fail and equip them with techniques they can use to reason about failure. Specifically, we discuss failure as either being caused by lack of reliability or lack of robustness. Differentiating the causes of failure in this way allows us to formally define why models fail from first principles and tie these definitions to engineering concepts and real-world deployment settings. Throughout the document we provide 1) a summary of important theoretic concepts in reliability and robustness, 2) a sampling current techniques that practitioners can utilize to reason about ML model reliability and robustness, and 3) examples that show how these concepts and techniques can apply to real-world settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。