Applied Conformal Prediction is a comprehensive and practical guide to one of the most powerful and rapidly evolving frameworks in machine learning: Conformal Prediction (CP).
Written by Valery Manokhin, who completed his PhD under Vladimir Vovk, the creator of Conformal Prediction, and has been one of its most prominent advocates for years, this book reflects deep expertise and commitment to the field. Manokhin's widely followed "Awesome Conformal Prediction" repository and his contributions to the global CP community have helped fuel its meteoric rise in research and industry.
Conformal Prediction is quickly becoming a must-have skill for anyone working in high-stakes, production-level AI systems. It provides rigorous, model-agnostic methods for quantifying uncertainty and constructing statistically valid prediction sets with guaranteed coverage. Unlike many traditional approaches, CP offers finite-sample guarantees without requiring unrealistic assumptions.
This book begins with the philosophical and mathematical origins of CP and walks you through its key components: exchangeability, nonconformity scores, prediction regions, inductive and adaptive variants, and beyond. It then explores cutting-edge research on:
- Classification
- Classifier calibration
- Regression
- Time Series and Forecasting (e.g., EnbPI, blockwise CP)
- Deep Learning Integration (NLP, CV, transformers)
- Weighted CP for covariate shift
- Software tools
- And much more
Whether you're a practitioner building risk-sensitive systems or a researcher exploring the limits of statistical inference, Applied Conformal Prediction is your definitive resource.
Preorder now to lock in the lowest price. You'll get early access to chapters as they’re released, and all future updates will be included. The price will increase significantly as more chapters are added.
