User-friendly Introduction to PAC-Bayes Bounds

Author:   Pierre Alquier
Publisher:   now publishers Inc
ISBN:  

9781638283263


Pages:   144
Publication Date:   22 January 2024
Format:   Paperback
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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User-friendly Introduction to PAC-Bayes Bounds


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Overview

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.

Full Product Details

Author:   Pierre Alquier
Publisher:   now publishers Inc
Imprint:   now publishers Inc
Weight:   0.213kg
ISBN:  

9781638283263


ISBN 10:   1638283265
Pages:   144
Publication Date:   22 January 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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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