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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    A Paperback / softback by Pierre Alquier

    £76.95

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    Order before 4pm today for delivery by Mon 19 Oct 2026.

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      Book details

      Publisher now publishers Inc
      Published 22 January 2024
      ISBN-13 9781638283263
      978-1638283263
      ISBN-10 1638283265

      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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