Description

Book Synopsis
Probably almost correct (PAC) bounds have been an intensive field of research over the last two decades. Hundreds of papers have been published and much progress has been made resulting in PAC-Bayes bounds becoming an important technique in machine learning.The proliferation of research has made the field for a newcomer somewhat daunting. In this tutorial, the author guides the reader through the topic’s complexity and large body of publications. Covering both empirical and oracle PAC-bounds, this book serves as a primer for students and researchers who want to get to grips quickly with the subject. It provides a friendly introduction that illuminates the basic theory and points to the most important publications to gain deeper understanding of any particular aspect.

Table of Contents
  • 1. Introduction
  • 2. First Step in the PAC-Bayes World
  • 3. Tight and Non-vacuous PAC-Bayes Bounds
  • 4. PAC-Bayes Oracle Inequalities and Fast Rates
  • 5. Beyond “Bounded Loss” and “i.i.d. Observations”
  • 6. Related Approaches in Statistics and Machine Learning Theory
  • 7. Conclusion
  • Acknowledgements
  • References

User-friendly Introduction to PAC-Bayes Bounds

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Order before 4pm today for delivery by Wed 14 Jan 2026.

A Paperback / softback by Pierre Alquier

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    View other formats and editions of User-friendly Introduction to PAC-Bayes Bounds by Pierre Alquier

    Publisher: now publishers Inc
    Publication Date: 22/01/2024
    ISBN13: 9781638283263, 978-1638283263
    ISBN10: 1638283265
    Also in:
    Machine learning Mathematical theory of computation Bayesian inference

    Description

    Book Synopsis
    Probably almost correct (PAC) bounds have been an intensive field of research over the last two decades. Hundreds of papers have been published and much progress has been made resulting in PAC-Bayes bounds becoming an important technique in machine learning.The proliferation of research has made the field for a newcomer somewhat daunting. In this tutorial, the author guides the reader through the topic’s complexity and large body of publications. Covering both empirical and oracle PAC-bounds, this book serves as a primer for students and researchers who want to get to grips quickly with the subject. It provides a friendly introduction that illuminates the basic theory and points to the most important publications to gain deeper understanding of any particular aspect.

    Table of Contents
    • 1. Introduction
    • 2. First Step in the PAC-Bayes World
    • 3. Tight and Non-vacuous PAC-Bayes Bounds
    • 4. PAC-Bayes Oracle Inequalities and Fast Rates
    • 5. Beyond “Bounded Loss” and “i.i.d. Observations”
    • 6. Related Approaches in Statistics and Machine Learning Theory
    • 7. Conclusion
    • Acknowledgements
    • References

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